Excess Mortality And Vaccines In Europe (v2)¶
Author: Justin Garza
Date: See below
Description:
This notebook explores excess mortality across Europe, analyzing statistical trends and investigating potential causes through data visualization and interpretation.
Content Warning:
If you find discussions of death or its underlying factors distressing, please proceed with caution or consider whether this content is right for you.
from datetime import datetime
from IPython.display import display
from IPython.display import Markdown as MD
current_date = datetime.now().strftime('%Y-%m-%d')
version = datetime.now().strftime('%Y%m%d.%H%M')
display(MD(f"**Date:** {current_date}"))
display(MD(f"**version:** {version}"))
Date: 2025-04-06
version: 20250406.2149
Table of Contents¶
import os
import json5 as json
from IPython.display import display, HTML
from IPython.display import Markdown as MD
nb_path = os.path.join(os.getcwd(),'main.ipynb')
# Read the notebook
with open(nb_path, 'r', encoding='utf-8') as f:
nb_data = json.load(f)
# Extract headers from markdown cells
outline = []
for cell in nb_data.get("cells", []):
if cell["cell_type"] == "markdown":
for line in cell["source"]:
if line.startswith("#"): # Markdown header
level = line.count("#") # Determine header level
title = line.lstrip("#").strip()
outline.append({
'text':title,
'level':level-1,
'link': f"index.html#{title.replace(' ','-')}"
})
for i in outline:
display(HTML(f'<a href=\'{i["link"]}\'>{"#" * i["level"]} {i["text"]}</a>'))
# print(f'<a href=\'{i["link"]}\'>{"#" * i["level"]}{i["text"]}</a>')
Prerequisites¶
Scientific Method¶
The scientific method is a systematic approach to investigating natural phenomena, acquiring knowledge, and testing hypotheses. It consists of the following key steps:
Observation
- Identify a problem or phenomenon that needs explanation.
- Gather initial data through direct observation or research.
Hypothesis
- Propose a testable and falsifiable explanation (a hypothesis).
- Example: "If plants receive more sunlight, then they will grow taller."
Experimentation
- Design and conduct controlled experiments to test the hypothesis.
- Include independent and dependent variables, control groups, and repeatable procedures.
Conclusion
- Determine whether the data supports or refutes the hypothesis.
- Modify or refine the hypothesis if necessary.
Replication
- Repeat experiments to verify results.
- Publish findings for scrutiny by the scientific community.
Rant!: Peer Review is Flawed
Imagine a mechanic repairs my car and then writes a paper about it.
Other mechanics review and approve the paper.
But when I get my car back, it still won’t start.
The mechanic insists, "But my paper was peer-reviewed!"
No matter how many experts approved the paper, what really matters is whether the experiment—getting the car to run—actually worked.
The scientific method ensures objectivity, reliability, and accuracy in scientific inquiry. It is an iterative process, meaning that conclusions can lead to new questions and further investigations.
Logical Fallacies¶
Logical fallacies are errors in reasoning that weaken arguments. They can be categorized into formal (structural errors) and informal (content errors).
| Type | Fallacy | Description |
|---|---|---|
| Formal | Affirming the Consequent | Assuming that if P → Q and Q is true, then P must be true. |
| Denying the Antecedent | Assuming that if P → Q and P is false, then Q must be false. | |
| Non-Sequitur | The conclusion does not logically follow from the premises. | |
| Informal – Relevance | Ad Hominem | Attacking the person instead of the argument. |
| Straw Man | Misrepresenting an argument to make it easier to attack. | |
| Red Herring | Diverting attention with an irrelevant point. | |
| Appeal to Authority | Claiming something is true because an authority said so. | |
| Appeal to Emotion | Using emotions instead of logic to argue a point. | |
| Informal – Causation & Presumption | Post Hoc Ergo Propter Hoc | Assuming that correlation implies causation. |
| Slippery Slope | Claiming one action will lead to extreme consequences. | |
| False Dilemma | Presenting only two options when more exist. | |
| Begging the Question | Using circular reasoning. | |
| False Equivalence | Treating two things as equal when they are not. | |
| Hasty Generalization | Drawing a conclusion from insufficient evidence. | |
| No True Scotsman | Excluding counterexamples by redefining a group. |
Logical fallacies can make arguments misleading or invalid. Identifying them helps improve critical thinking and debate skills.
About Appeal to Authority¶
DOCTORS USED TO PRESCRIBE CIGARETTES


And More ...¶
- Bloodletting
- Lobotomies
- Radium and Mercury Treatments
- Thalidomide for Morning Sickness
- Cocaine and Heroin as Medicine
- X-Ray Shoe Fitting
- Forceps and Twilight Sleep in Childbirth
- Tapeworm Diet Pills
- Electroshock Therapy (Overuse)
Therefore Doctors need to provide something more than just saying they are an authority on a subject.
About Post Hoc Ergo Propter Hoc (Correlation vs Causation)¶
Flipping a switch and seeing the light turn on implies causation, not just correlation.
- Suggesting mere correlation in this case would seem absurd.
- without repetedly flipping the switch to test it ... we would still stay causation because:
- Mechanistic Understanding
- We know how light switches work; they complete a circuit to turn the light on.
- Temporal Order
- The switch is flipped first, and the light turns on immediately after, fitting the cause-effect pattern.
- Location Relevance
- in this example, we are assuming the light came on in the same house where the light switch was (not a different building across town)
- Alternative Explanations
- A purely correlational event would imply something external
- (e.g., a power surge or someone else activating the light), which is far less probable.
- A purely correlational event would imply something external
- Mechanistic Understanding
Later on we will talk about vaccines, and if they work ideally they should have an impact on deaths within a country.
- Mechanistic Understanding
- we all know that vaccines save lives and are safe
- from constantly being told that in schools and the medical system
- we all know that vaccines save lives and are safe
- Temporal Order
- vaccines doses are given out, then there should be less people dying.
- Location Relevance
- we should see the location relevance (european countries) that took more vaccines vs less vaccines.
- unforetunatly, we don't have perfect data so that a country that took zero vaccines did not exists within the dataset.
- we should see the location relevance (european countries) that took more vaccines vs less vaccines.
- Alternative Explanations
- Plausible alternative explanations may exist, but they require evidence to support them.
- Suggesting an alternative without supporting evidence is a red herring, distracting from the most logical conclusion.
- Plausible alternatives should also be relavent to the scale we are refering to.
- As a rule of thumb a small cause - small event, big cause - big event.
- Plausible alternative explanations may exist, but they require evidence to support them.
- Mechanistic Understanding
Observation¶
There were two sides when it comes to the vaccines
- The covid-19 vaccines were bad, and cause side effects (including death)
- The covid-19 vaccines were good and saved lives, and is safe.
News Articles & Headlines¶
The One side of this can easily be seen in the news headlines, using logical fallacies
Ad Hominem Attacks¶
- CDC Warns of 'Pandemic of the Unvaccinated'
- Covid: French uproar as Macron vows to 'piss off' unvaccinated
- Don Lemon Unloads on Unvaxxed: We Have to ‘Do Things For The Greater Good Of Society, Not For Idiots’
- People Who Skip Vaccinations 'Incredibly Selfish' Experts Say
- "If you're willing to walk among us unvaccinated, you are an enemy." - Gene Simmons, co-lead singer and co-founder of KISS
- plague rats
- selfish
- anti-science
- ignorant
- irresponsible.
Appeals to Authority¶
- Pope Francis urges people to get vaccinated against Covid-19
- Former Presidents Obama, Bush and Clinton volunteer to get coronavirus vaccine publicly to prove it’s safe
- FDA Approves First COVID-19 Vaccine
Appeal to Emotions¶
Denial of Aid¶
One side of this topic also had the power to denie aid and services.
- D.J. Ferguson
- Service Denied: Heart transplant at Brigham and Women’s Hospital (2022).
- Reason: Refused COVID-19 vaccine, a hospital requirement.
- Leilani Lutali
- Service Denied: Kidney transplant at UCHealth (2021).
- Reason: Opposed vaccine due to religious beliefs; hospital mandated it.
- Adaline Deal
- Service Denied: Heart transplant list at Cincinnati Children’s Hospital (2025).
- Reason: Parents refused COVID-19 and flu vaccines on religious grounds.
- Jennifer Bridges
- Service Denied: Employment at Houston Methodist Hospital (2021).
- Reason: Refused vaccine mandate; fired.
- Northwell Health Employees (1,400 individuals)
- Service Denied: Employment (2021).
- Reason: Refused vaccine mandate at New York healthcare provider.
- General Cases of Unemployment Benefit Denials
- Service Denied: Unemployment benefits (2021-2025).
- Reason: Fired or quit over vaccine mandates; often deemed “misconduct.”
Hypothesis¶
Given the time elapsed since the COVID-19 pandemic, can we assess the long-term effectiveness of COVID-19 vaccines through available data ?
Note: This might not be a binary result (even though there are pretty binary sides),
| Value | Effect | Description/Correlation-Suggestion |
|---|---|---|
| 1.0 | Positive effect | The vaccine could have a possitive effect in saving lives and getting society back to normal. |
| 0.5 | Slightly positive effect | The Vaccine could have a slightly positive effect |
| 0.0 | Null/No effect | The Vaccine could have a null/no effect |
| -0.5 | Slightly negative effect | The Vaccine could have a slightly negative effect |
| -1.0 | Negative effect | The Vaccine could have a negative effect |
... and of course we might dissagree on the results, and/or further research might need to be done.
below we will be¶
- Experimentation obtaining data, and producing some charts/graphs.
- this can't be a real experiment with a control group, since we are limited to the data,but i hope there is enough data to come to some conclusion.
- Conclusion drawing the best conclusion we can from the data
- Replication challening you (the audience) to do approve or disprove the conclusion with your own data science approach.
Setup¶
In this section, we prepare the notebook by importing necessary libraries, configuring settings, and setting up directories for data and outputs. The setup ensures the environment is ready for data analysis and visualization.
# this code to will import all the things i need for this notebook
import os
import re
import math
import numpy as np
import pandas as pd
# for the notebook rendering
from IPython.display import display, HTML
from IPython.display import Markdown as MD
# Graphs and Charts
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
# use to export plotly graphs
import plotly.io as pio
#misc
from scipy.stats import spearmanr, kendalltau
import pycountry
# pandas Settings/Options
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option('display.width', 9000)
pd.set_option('max_colwidth', 400)
pd.set_option('display.float_format', '{:.3f}'.format)
# colormap
heatmapCM = sns.color_palette('Spectral_r', as_cmap=True)
heatmapCM1 = sns.color_palette('Spectral_r', as_cmap=True)
heatmapCM2 = sns.color_palette('coolwarm', as_cmap=True)
heatmapCM3 = sns.color_palette('viridis_r', as_cmap=True)
heatmapCM4 = sns.color_palette('coolwarm_r', as_cmap=True)
## directories
DIR = os.getcwd()
print(f'{DIR=}')
DataDIR = os.path.join(DIR,'data')
OutDIR = os.path.join(DIR,'docs')
if not os.path.exists(DataDIR):
print('***DATA FOLDER IS MISSING***')
if not os.path.exists(OutDIR):
os.makedirs(OutDIR)
DIR='C:\\Users\\JGarza\\GitHub\\Excess_Mortality_And_Vaccines_In_Europe'
Helping Functions¶
This section defines utility functions that streamline repetitive tasks and improve code readability. These functions will be used throughout the notebook to simplify operations, enhance modularity, and reduce redundancy.
def df_column_uniquify(df):
'''
renames columns that are the same
'''
df_columns = df.columns
new_columns = []
for item in df_columns:
counter = 0
newitem = item
while newitem in new_columns:
counter += 1
newitem = "{}_{}".format(item, counter)
new_columns.append(newitem)
df.columns = new_columns
return df
def abbr_to_isoalpha3(abbr):
"""
Convert a European country ISO Alpha-2 code to ISO Alpha-3 code.
Parameters:
abbreviation (str): ISO Alpha-2 country code (e.g., 'DE' for Germany).
Returns:
str: ISO Alpha-3 country code (e.g., 'DEU'), or None if not found.
"""
try:
country = pycountry.countries.get(alpha_2=abbr.upper())
if country:
return country.alpha_3
else:
return None
except KeyError:
return None
# this if for converting between the abbreviation andand the names of the countries
country_dict = {
"BE": "Belgium",
"BG": "Bulgaria",
"CZ": "Czechia",
"DK": "Denmark",
"DE": "Germany",
"EE": "Estonia",
"IE": "Ireland",
"EL": "Greece",
"ES": "Spain",
"FR": "France",
"HR": "Croatia",
"IT": "Italy",
"CY": "Cyprus",
"LV": "Latvia",
"LT": "Lithuania",
"LU": "Luxembourg",
"HU": "Hungary",
"MT": "Malta",
"NL": "Netherlands",
"AT": "Austria",
"PL": "Poland",
"PT": "Portugal",
"RO": "Romania",
"SI": "Slovenia",
"SK": "Slovakia",
"FI": "Finland",
"SE": "Sweden",
"IS": "Iceland",
"LI": "Liechtenstein",
"NO": "Norway",
"CH": "Switzerland",
"UK": "United Kingdom",
"ME": "Montenegro",
"GE": "Georgia",
"AL": "Albania",
"RS": "Serbia",
"AD": "Andorra",
"AM": "Armenia"
}
def abbr_to_name(abbreviation):
return country_dict.get(abbreviation.upper(), "Abbreviation not found")
def name_to_abbr(name):
reverse_dict = {v: k for k, v in country_dict.items()}
return reverse_dict.get(name, "Unknown")
# testing
print( abbr_to_name("BE") ) # Output: 'Belgium'
print( name_to_abbr("Belgium") ) # Output: 'BE'
Belgium BE
def bar(num,denom=100.0,length=25,fillchar='#',emptychar='_'):
fillnum = ((int)( (num/denom) * length))
return '[' + ( fillnum * fillchar ).ljust(length,emptychar) + ']' # + f" {(num/denom)*100.0:.2f}% "
print(bar(5,50))
print(bar(25,50))
print(bar(40,50))
print(bar(50,50))
[##_______________________] [############_____________] [####################_____] [#########################]
Import and Clean Data¶
death data¶
Getting the Data¶
- go to Europa.eu - Database
- choose
- Population and social conditions
- Demography, population stock and balance
- Deaths by week – special data collection
- Deaths by week, sex and 20-year age group
- Click the little table
- customize the data
- Customize your dataset -> Time -> From - to
- From: 2015-W01
- To: [Current or Max]
- Move the
Age ClassunderGeopolitical entity (reporting)
- Customize your dataset -> Time -> From - to
- Click
download(as a spreadsheet) and place the file in the.\datafolder


variables¶
- dd = death data (by year and week)
- ddy = death data (by year)
- ddn = normalized deaths (by year)
# getting the data
dd = pd.read_excel(os.path.join(DataDIR,"demo_r_mwk_20__custom_15613355_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")
# remove the headers
dd = dd.iloc[7::]
# drop the bad columns
for c in dd.columns:
if pd.isnull(dd.at[7,c]):
dd = dd.drop(columns=[c])
# rename time columns
for c in dd.columns:
name = dd.at[7,c]
dd = dd.rename(columns={c: name})
# make the duplicate column names unique
dd = df_column_uniquify(dd)
# # rename the first two columns
dd = dd.rename(columns={'TIME': 'abbr'})
dd = dd.rename(columns={'TIME_1':'name'})
dd = dd.rename(columns={'TIME_2':'agegrp'})
dd = dd.rename(columns={'TIME_3':'agegrp_desc'})
# drop, replace, reset index,
dd = dd.drop([7,8,9])
dd = dd.replace(to_replace=':', value=None)
dd = dd.reset_index(drop=True)
# display(dd.head(3))
display(dd.tail(10))
C:\Users\JGarza\pythons\Python312\Lib\site-packages\openpyxl\styles\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default
warn("Workbook contains no default style, apply openpyxl's default")
| abbr | name | agegrp | agegrp_desc | 2015-W01 | 2015-W02 | 2015-W03 | 2015-W04 | 2015-W05 | 2015-W06 | 2015-W07 | 2015-W08 | 2015-W09 | 2015-W10 | 2015-W11 | 2015-W12 | 2015-W13 | 2015-W14 | 2015-W15 | 2015-W16 | 2015-W17 | 2015-W18 | 2015-W19 | 2015-W20 | 2015-W21 | 2015-W22 | 2015-W23 | 2015-W24 | 2015-W25 | 2015-W26 | 2015-W27 | 2015-W28 | 2015-W29 | 2015-W30 | 2015-W31 | 2015-W32 | 2015-W33 | 2015-W34 | 2015-W35 | 2015-W36 | 2015-W37 | 2015-W38 | 2015-W39 | 2015-W40 | 2015-W41 | 2015-W42 | 2015-W43 | 2015-W44 | 2015-W45 | 2015-W46 | 2015-W47 | 2015-W48 | 2015-W49 | 2015-W50 | 2015-W51 | 2015-W52 | 2015-W53 | 2016-W01 | 2016-W02 | 2016-W03 | 2016-W04 | 2016-W05 | 2016-W06 | 2016-W07 | 2016-W08 | 2016-W09 | 2016-W10 | 2016-W11 | 2016-W12 | 2016-W13 | 2016-W14 | 2016-W15 | 2016-W16 | 2016-W17 | 2016-W18 | 2016-W19 | 2016-W20 | 2016-W21 | 2016-W22 | 2016-W23 | 2016-W24 | 2016-W25 | 2016-W26 | 2016-W27 | 2016-W28 | 2016-W29 | 2016-W30 | 2016-W31 | 2016-W32 | 2016-W33 | 2016-W34 | 2016-W35 | 2016-W36 | 2016-W37 | 2016-W38 | 2016-W39 | 2016-W40 | 2016-W41 | 2016-W42 | 2016-W43 | 2016-W44 | 2016-W45 | 2016-W46 | 2016-W47 | 2016-W48 | 2016-W49 | 2016-W50 | 2016-W51 | 2016-W52 | 2017-W01 | 2017-W02 | 2017-W03 | 2017-W04 | 2017-W05 | 2017-W06 | 2017-W07 | 2017-W08 | 2017-W09 | 2017-W10 | 2017-W11 | 2017-W12 | 2017-W13 | 2017-W14 | 2017-W15 | 2017-W16 | 2017-W17 | 2017-W18 | 2017-W19 | 2017-W20 | 2017-W21 | 2017-W22 | 2017-W23 | 2017-W24 | 2017-W25 | 2017-W26 | 2017-W27 | 2017-W28 | 2017-W29 | 2017-W30 | 2017-W31 | 2017-W32 | 2017-W33 | 2017-W34 | 2017-W35 | 2017-W36 | 2017-W37 | 2017-W38 | 2017-W39 | 2017-W40 | 2017-W41 | 2017-W42 | 2017-W43 | 2017-W44 | 2017-W45 | 2017-W46 | 2017-W47 | 2017-W48 | 2017-W49 | 2017-W50 | 2017-W51 | 2017-W52 | 2018-W01 | 2018-W02 | 2018-W03 | 2018-W04 | 2018-W05 | 2018-W06 | 2018-W07 | 2018-W08 | 2018-W09 | 2018-W10 | 2018-W11 | 2018-W12 | 2018-W13 | 2018-W14 | 2018-W15 | 2018-W16 | 2018-W17 | 2018-W18 | 2018-W19 | 2018-W20 | 2018-W21 | 2018-W22 | 2018-W23 | 2018-W24 | 2018-W25 | 2018-W26 | 2018-W27 | 2018-W28 | 2018-W29 | 2018-W30 | 2018-W31 | 2018-W32 | 2018-W33 | 2018-W34 | 2018-W35 | 2018-W36 | 2018-W37 | 2018-W38 | 2018-W39 | 2018-W40 | 2018-W41 | 2018-W42 | 2018-W43 | 2018-W44 | 2018-W45 | 2018-W46 | 2018-W47 | 2018-W48 | 2018-W49 | 2018-W50 | 2018-W51 | 2018-W52 | 2019-W01 | 2019-W02 | 2019-W03 | 2019-W04 | 2019-W05 | 2019-W06 | 2019-W07 | 2019-W08 | 2019-W09 | 2019-W10 | 2019-W11 | 2019-W12 | 2019-W13 | 2019-W14 | 2019-W15 | 2019-W16 | 2019-W17 | 2019-W18 | 2019-W19 | 2019-W20 | 2019-W21 | 2019-W22 | 2019-W23 | 2019-W24 | 2019-W25 | 2019-W26 | 2019-W27 | 2019-W28 | 2019-W29 | 2019-W30 | 2019-W31 | 2019-W32 | 2019-W33 | 2019-W34 | 2019-W35 | 2019-W36 | 2019-W37 | 2019-W38 | 2019-W39 | 2019-W40 | 2019-W41 | 2019-W42 | 2019-W43 | 2019-W44 | 2019-W45 | 2019-W46 | 2019-W47 | 2019-W48 | 2019-W49 | 2019-W50 | 2019-W51 | 2019-W52 | 2020-W01 | 2020-W02 | 2020-W03 | 2020-W04 | 2020-W05 | 2020-W06 | 2020-W07 | 2020-W08 | 2020-W09 | 2020-W10 | 2020-W11 | 2020-W12 | 2020-W13 | 2020-W14 | 2020-W15 | 2020-W16 | 2020-W17 | 2020-W18 | 2020-W19 | 2020-W20 | 2020-W21 | 2020-W22 | 2020-W23 | 2020-W24 | 2020-W25 | 2020-W26 | 2020-W27 | 2020-W28 | 2020-W29 | 2020-W30 | 2020-W31 | 2020-W32 | 2020-W33 | 2020-W34 | 2020-W35 | 2020-W36 | 2020-W37 | 2020-W38 | 2020-W39 | 2020-W40 | 2020-W41 | 2020-W42 | 2020-W43 | 2020-W44 | 2020-W45 | 2020-W46 | 2020-W47 | 2020-W48 | 2020-W49 | 2020-W50 | 2020-W51 | 2020-W52 | 2020-W53 | 2021-W01 | 2021-W02 | 2021-W03 | 2021-W04 | 2021-W05 | 2021-W06 | 2021-W07 | 2021-W08 | 2021-W09 | 2021-W10 | 2021-W11 | 2021-W12 | 2021-W13 | 2021-W14 | 2021-W15 | 2021-W16 | 2021-W17 | 2021-W18 | 2021-W19 | 2021-W20 | 2021-W21 | 2021-W22 | 2021-W23 | 2021-W24 | 2021-W25 | 2021-W26 | 2021-W27 | 2021-W28 | 2021-W29 | 2021-W30 | 2021-W31 | 2021-W32 | 2021-W33 | 2021-W34 | 2021-W35 | 2021-W36 | 2021-W37 | 2021-W38 | 2021-W39 | 2021-W40 | 2021-W41 | 2021-W42 | 2021-W43 | 2021-W44 | 2021-W45 | 2021-W46 | 2021-W47 | 2021-W48 | 2021-W49 | 2021-W50 | 2021-W51 | 2021-W52 | 2022-W01 | 2022-W02 | 2022-W03 | 2022-W04 | 2022-W05 | 2022-W06 | 2022-W07 | 2022-W08 | 2022-W09 | 2022-W10 | 2022-W11 | 2022-W12 | 2022-W13 | 2022-W14 | 2022-W15 | 2022-W16 | 2022-W17 | 2022-W18 | 2022-W19 | 2022-W20 | 2022-W21 | 2022-W22 | 2022-W23 | 2022-W24 | 2022-W25 | 2022-W26 | 2022-W27 | 2022-W28 | 2022-W29 | 2022-W30 | 2022-W31 | 2022-W32 | 2022-W33 | 2022-W34 | 2022-W35 | 2022-W36 | 2022-W37 | 2022-W38 | 2022-W39 | 2022-W40 | 2022-W41 | 2022-W42 | 2022-W43 | 2022-W44 | 2022-W45 | 2022-W46 | 2022-W47 | 2022-W48 | 2022-W49 | 2022-W50 | 2022-W51 | 2022-W52 | 2023-W01 | 2023-W02 | 2023-W03 | 2023-W04 | 2023-W05 | 2023-W06 | 2023-W07 | 2023-W08 | 2023-W09 | 2023-W10 | 2023-W11 | 2023-W12 | 2023-W13 | 2023-W14 | 2023-W15 | 2023-W16 | 2023-W17 | 2023-W18 | 2023-W19 | 2023-W20 | 2023-W21 | 2023-W22 | 2023-W23 | 2023-W24 | 2023-W25 | 2023-W26 | 2023-W27 | 2023-W28 | 2023-W29 | 2023-W30 | 2023-W31 | 2023-W32 | 2023-W33 | 2023-W34 | 2023-W35 | 2023-W36 | 2023-W37 | 2023-W38 | 2023-W39 | 2023-W40 | 2023-W41 | 2023-W42 | 2023-W43 | 2023-W44 | 2023-W45 | 2023-W46 | 2023-W47 | 2023-W48 | 2023-W49 | 2023-W50 | 2023-W51 | 2023-W52 | 2024-W01 | 2024-W02 | 2024-W03 | 2024-W04 | 2024-W05 | 2024-W06 | 2024-W07 | 2024-W08 | 2024-W09 | 2024-W10 | 2024-W11 | 2024-W12 | 2024-W13 | 2024-W14 | 2024-W15 | 2024-W16 | 2024-W17 | 2024-W18 | 2024-W19 | 2024-W20 | 2024-W21 | 2024-W22 | 2024-W23 | 2024-W24 | 2024-W25 | 2024-W26 | 2024-W27 | 2024-W28 | 2024-W29 | 2024-W30 | 2024-W31 | 2024-W32 | 2024-W33 | 2024-W34 | 2024-W35 | 2024-W36 | 2024-W37 | 2024-W38 | 2024-W39 | 2024-W40 | 2024-W41 | 2024-W42 | 2024-W43 | 2024-W44 | 2024-W45 | 2024-W46 | 2024-W47 | 2024-W48 | 2024-W49 | 2024-W50 | 2024-W51 | 2024-W52 | 2025-W01 | 2025-W02 | 2025-W03 | 2025-W04 | 2025-W05 | 2025-W06 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 228 | AM | Armenia | Y_LT20 | Less than 20 years | 10 | 1 | 18 | 5 | 15 | 21 | 11 | 8 | 9 | 14 | 11 | 7 | 11 | 13 | 5 | 8 | 9 | 13 | 14 | 13 | 10 | 8 | 13 | 11 | 13 | 10 | 17 | 17 | 12 | 4 | 12 | 13 | 15 | 14 | 11 | 16 | 19 | 16 | 11 | 7 | 13 | 6 | 9 | 6 | 17 | 9 | 10 | 14 | 9 | 8 | 14 | 14 | 11 | 6 | 12 | 12 | 12 | 14 | 17 | 12 | 9 | 10 | 6 | 9 | 11 | 13 | 20 | 26 | 22 | 7 | 10 | 12 | 13 | 15 | 13 | 19 | 11 | 8 | 11 | 10 | 16 | 15 | 17 | 8 | 7 | 13 | 6 | 8 | 13 | 13 | 8 | 8 | 10 | 8 | 7 | 15 | 18 | 8 | 13 | 20 | 10 | 17 | 9 | 8 | 10 | 0 | 18 | 12 | 10 | 9 | 7 | 12 | 10 | 10 | 5 | 11 | 7 | 12 | 11 | 12 | 9 | 10 | 13 | 11 | 14 | 5 | 5 | 6 | 12 | 12 | 13 | 11 | 10 | 11 | 7 | 12 | 4 | 12 | 11 | 11 | 11 | 13 | 11 | 14 | 11 | 10 | 7 | 11 | 9 | 2 | 12 | 7 | 15 | 9 | 4 | 8 | 8 | 1 | 11 | 5 | 7 | 12 | 8 | 13 | 11 | 11 | 3 | 13 | 8 | 8 | 11 | 8 | 14 | 11 | 3 | 7 | 16 | 8 | 8 | 11 | 12 | 3 | 10 | 9 | 13 | 17 | 2 | 4 | 8 | 9 | 5 | 7 | 11 | 14 | 10 | 12 | 8 | 3 | 8 | 11 | 10 | 6 | 13 | 4 | 9 | 5 | 5 | 11 | 6 | 0 | 7 | 4 | 8 | 8 | 14 | 3 | 7 | 17 | 10 | 12 | 5 | 5 | 6 | 6 | 3 | 8 | 7 | 7 | 8 | 8 | 9 | 11 | 7 | 9 | 9 | 4 | 13 | 7 | 10 | 6 | 16 | 8 | 9 | 9 | 7 | 4 | 7 | 7 | 9 | 8 | 9 | 7 | 7 | 5 | 7 | 8 | 9 | 6 | 9 | 6 | 6 | 1 | 7 | 9 | 7 | 12 | 11 | 12 | 14 | 10 | 10 | 5 | 11 | 6 | 10 | 6 | 6 | 10 | 12 | 14 | 16 | 8 | 7 | 13 | 6 | 10 | 6 | 8 | 8 | 6 | 11 | 9 | 9 | 6 | 3 | 8 | 10 | 8 | 5 | 4 | 10 | 13 | 20 | 121 | 101 | 124 | 85 | 87 | 86 | 72 | 32 | 30 | 32 | 20 | 28 | 52 | 54 | 51 | 38 | 41 | 33 | 29 | 12 | 7 | 9 | 8 | 7 | 18 | 17 | 11 | 16 | 13 | 11 | 8 | 11 | 16 | 7 | 9 | 12 | 12 | 15 | 17 | 9 | 18 | 12 | 7 | 6 | 14 | 22 | 11 | 11 | 6 | 10 | 5 | 10 | 11 | 6 | 24 | 10 | 4 | 14 | 11 | 18 | 6 | 6 | 8 | 10 | 8 | 11 | 8 | 6 | 8 | 12 | 8 | 15 | 8 | 9 | 6 | 8 | 8 | 12 | 5 | 6 | 5 | 9 | 6 | 10 | 12 | 13 | 7 | 9 | 7 | 6 | 5 | 9 | 8 | 9 | 8 | 6 | 7 | 13 | 3 | 16 | 23 | 19 | 31 | 31 | 14 | 18 | 16 | 7 | 14 | 9 | 14 | 10 | 14 | 8 | 11 | 2 | 9 | 10 | 10 | 11 | 6 | 9 | 8 | 11 | 7 | 14 | 6 | 13 | 15 | 11 | 7 | 9 | 5 | 2 | 10 | 18 | 9 | 7 | 11 | 12 | 7 | 4 | 5 | 9 | 10 | 8 | 6 | 12 | 11 | 8 | 14 | 7 | 17 | 10 | 8 | 3 | 4 | 13 | 8 | 3 | 8 | 9 | 7 | 12 | 6 | 14 | 6 | 7 | 11 | 8 | 4 | 2 | 6 | 7 | 6 | 9 | 5 | 10 | 7 | 6 | None | None | None | None | 7 | 6 | 4 | 5 | 7 | 4 | 7 | 10 | 8 | 8 | 9 | 7 | 6 | 10 | 9 | 4 | 7 | 4 | 8 | 6 | 6 | 7 | 4 | 12 | 8 | 11 | 1 | 3 | 11 | 9 | 7 | 8 | 6 | 7 | 10 | None | None | None | None | None | None |
| 229 | AM | Armenia | Y20-39 | From 20 to 39 years | 3 | 3 | 28 | 12 | 18 | 13 | 13 | 18 | 12 | 20 | 15 | 13 | 18 | 14 | 9 | 13 | 15 | 20 | 7 | 11 | 17 | 11 | 14 | 16 | 22 | 8 | 12 | 22 | 20 | 14 | 15 | 17 | 20 | 12 | 22 | 15 | 14 | 12 | 11 | 20 | 17 | 11 | 12 | 9 | 16 | 18 | 15 | 22 | 8 | 22 | 11 | 19 | 9 | 4 | 24 | 17 | 13 | 17 | 20 | 17 | 10 | 6 | 8 | 12 | 17 | 19 | 23 | 26 | 18 | 13 | 10 | 9 | 18 | 15 | 13 | 15 | 12 | 15 | 12 | 17 | 8 | 14 | 15 | 12 | 21 | 10 | 9 | 9 | 19 | 12 | 14 | 13 | 15 | 11 | 18 | 12 | 18 | 25 | 7 | 15 | 14 | 20 | 18 | 11 | 9 | 4 | 22 | 16 | 17 | 13 | 12 | 14 | 16 | 13 | 11 | 16 | 14 | 8 | 9 | 15 | 11 | 11 | 16 | 15 | 15 | 9 | 12 | 18 | 14 | 14 | 14 | 11 | 10 | 11 | 15 | 16 | 12 | 22 | 14 | 13 | 13 | 10 | 4 | 12 | 8 | 7 | 12 | 10 | 11 | 11 | 15 | 13 | 11 | 27 | 7 | 8 | 9 | 1 | 21 | 17 | 12 | 11 | 13 | 10 | 6 | 16 | 3 | 16 | 13 | 19 | 12 | 15 | 16 | 15 | 17 | 13 | 12 | 12 | 10 | 16 | 15 | 12 | 12 | 10 | 12 | 19 | 9 | 10 | 24 | 8 | 20 | 17 | 14 | 14 | 9 | 13 | 16 | 5 | 15 | 17 | 17 | 8 | 14 | 15 | 13 | 16 | 22 | 10 | 16 | 0 | 4 | 10 | 6 | 16 | 7 | 9 | 14 | 16 | 12 | 7 | 14 | 13 | 10 | 13 | 15 | 8 | 11 | 16 | 15 | 11 | 20 | 9 | 4 | 11 | 18 | 10 | 9 | 17 | 9 | 17 | 13 | 5 | 14 | 13 | 14 | 13 | 13 | 11 | 9 | 16 | 11 | 11 | 9 | 11 | 17 | 12 | 10 | 10 | 15 | 11 | 11 | 7 | 12 | 9 | 15 | 15 | 19 | 13 | 10 | 7 | 13 | 16 | 6 | 8 | 7 | 12 | 17 | 6 | 8 | 13 | 14 | 9 | 5 | 15 | 19 | 15 | 15 | 11 | 9 | 17 | 15 | 12 | 16 | 11 | 11 | 17 | 20 | 13 | 17 | 6 | 18 | 23 | 38 | 248 | 233 | 187 | 128 | 150 | 94 | 76 | 42 | 44 | 42 | 35 | 35 | 78 | 58 | 47 | 48 | 51 | 35 | 34 | 21 | 10 | 10 | 20 | 19 | 22 | 26 | 33 | 16 | 24 | 11 | 11 | 13 | 21 | 9 | 13 | 14 | 17 | 16 | 14 | 23 | 21 | 18 | 14 | 19 | 14 | 18 | 20 | 14 | 14 | 13 | 11 | 19 | 16 | 15 | 20 | 21 | 22 | 23 | 12 | 16 | 15 | 16 | 5 | 7 | 17 | 13 | 7 | 10 | 13 | 12 | 10 | 14 | 18 | 9 | 8 | 6 | 11 | 19 | 9 | 9 | 17 | 9 | 17 | 12 | 15 | 10 | 11 | 12 | 13 | 15 | 16 | 11 | 15 | 8 | 13 | 17 | 10 | 20 | 13 | 14 | 30 | 28 | 28 | 28 | 22 | 18 | 15 | 7 | 22 | 13 | 28 | 15 | 17 | 10 | 8 | 9 | 9 | 19 | 23 | 10 | 16 | 18 | 11 | 13 | 11 | 11 | 8 | 16 | 13 | 15 | 14 | 15 | 7 | 8 | 14 | 12 | 7 | 11 | 13 | 15 | 11 | 13 | 8 | 10 | 7 | 8 | 12 | 20 | 19 | 20 | 12 | 9 | 12 | 19 | 12 | 11 | 12 | 11 | 7 | 11 | 13 | 10 | 7 | 10 | 17 | 8 | 11 | 4 | 18 | 16 | 14 | 14 | 13 | 15 | 11 | 14 | 13 | 12 | 16 | 18 | None | None | None | None | 20 | 11 | 7 | 10 | 12 | 8 | 7 | 10 | 15 | 20 | 12 | 11 | 10 | 10 | 20 | 7 | 8 | 14 | 12 | 22 | 11 | 9 | 7 | 16 | 15 | 13 | 12 | 11 | 13 | 9 | 16 | 5 | 10 | 16 | 14 | None | None | None | None | None | None |
| 230 | AM | Armenia | Y40-59 | From 40 to 59 years | 34 | 38 | 168 | 108 | 92 | 89 | 82 | 98 | 95 | 99 | 91 | 102 | 97 | 71 | 84 | 85 | 56 | 86 | 92 | 113 | 74 | 56 | 87 | 86 | 91 | 72 | 64 | 75 | 94 | 62 | 75 | 66 | 77 | 65 | 84 | 88 | 60 | 77 | 66 | 72 | 76 | 83 | 74 | 91 | 64 | 90 | 93 | 88 | 82 | 87 | 93 | 90 | 61 | 55 | 146 | 125 | 74 | 103 | 72 | 83 | 97 | 70 | 70 | 86 | 90 | 77 | 78 | 86 | 79 | 78 | 67 | 59 | 73 | 85 | 77 | 69 | 61 | 77 | 77 | 82 | 82 | 73 | 76 | 78 | 80 | 66 | 64 | 76 | 72 | 77 | 87 | 64 | 54 | 84 | 72 | 64 | 75 | 76 | 92 | 96 | 81 | 82 | 90 | 88 | 81 | 30 | 154 | 98 | 65 | 90 | 88 | 83 | 103 | 87 | 77 | 89 | 73 | 54 | 78 | 84 | 78 | 78 | 69 | 49 | 70 | 68 | 60 | 64 | 73 | 62 | 76 | 50 | 71 | 57 | 61 | 65 | 72 | 73 | 62 | 61 | 51 | 58 | 62 | 78 | 76 | 87 | 66 | 66 | 70 | 77 | 75 | 59 | 73 | 75 | 78 | 83 | 57 | 17 | 97 | 92 | 69 | 69 | 94 | 72 | 74 | 79 | 55 | 93 | 66 | 70 | 71 | 81 | 79 | 71 | 70 | 78 | 75 | 72 | 57 | 77 | 67 | 71 | 48 | 62 | 66 | 70 | 63 | 66 | 63 | 74 | 69 | 68 | 68 | 70 | 55 | 73 | 49 | 47 | 73 | 67 | 73 | 66 | 84 | 82 | 76 | 76 | 56 | 81 | 72 | 10 | 47 | 62 | 75 | 83 | 65 | 84 | 99 | 72 | 62 | 83 | 82 | 85 | 72 | 77 | 79 | 60 | 54 | 60 | 86 | 69 | 54 | 61 | 57 | 63 | 67 | 45 | 63 | 70 | 62 | 54 | 60 | 46 | 70 | 76 | 59 | 45 | 55 | 53 | 56 | 64 | 62 | 47 | 63 | 69 | 73 | 76 | 92 | 84 | 75 | 80 | 80 | 37 | 92 | 77 | 95 | 77 | 59 | 79 | 68 | 72 | 71 | 51 | 53 | 63 | 49 | 58 | 58 | 53 | 69 | 62 | 68 | 82 | 52 | 87 | 94 | 83 | 85 | 79 | 78 | 75 | 72 | 77 | 66 | 61 | 55 | 68 | 74 | 72 | 69 | 72 | 54 | 64 | 72 | 138 | 166 | 161 | 143 | 153 | 139 | 124 | 119 | 131 | 110 | 65 | 50 | 161 | 96 | 92 | 81 | 102 | 69 | 65 | 75 | 68 | 72 | 76 | 92 | 72 | 72 | 104 | 87 | 73 | 76 | 80 | 73 | 80 | 56 | 64 | 58 | 78 | 66 | 68 | 67 | 43 | 64 | 57 | 62 | 61 | 66 | 72 | 65 | 80 | 78 | 73 | 93 | 105 | 134 | 124 | 117 | 124 | 90 | 91 | 92 | 86 | 76 | 58 | 59 | 94 | 82 | 68 | 91 | 73 | 87 | 77 | 73 | 54 | 73 | 62 | 62 | 55 | 46 | 49 | 63 | 71 | 58 | 61 | 64 | 55 | 54 | 48 | 50 | 46 | 59 | 50 | 58 | 50 | 54 | 69 | 56 | 59 | 50 | 47 | 56 | 75 | 58 | 62 | 64 | 49 | 62 | 57 | 51 | 55 | 52 | 64 | 59 | 78 | 42 | 50 | 55 | 79 | 63 | 76 | 60 | 61 | 57 | 58 | 66 | 43 | 52 | 58 | 42 | 68 | 59 | 63 | 61 | 51 | 42 | 52 | 49 | 45 | 45 | 55 | 57 | 59 | 39 | 57 | 50 | 70 | 60 | 58 | 72 | 54 | 45 | 68 | 49 | 49 | 57 | 43 | 65 | 57 | 54 | 54 | 37 | 58 | 53 | 40 | 62 | 51 | 46 | 60 | 37 | 73 | 77 | 55 | 63 | 72 | 72 | 62 | 62 | 70 | 59 | 74 | 44 | None | None | None | None | 38 | 42 | 44 | 70 | 49 | 60 | 51 | 43 | 55 | 61 | 54 | 51 | 53 | 49 | 68 | 49 | 54 | 55 | 52 | 55 | 45 | 56 | 63 | 46 | 55 | 62 | 51 | 58 | 54 | 59 | 52 | 49 | 45 | 56 | 60 | None | None | None | None | None | None |
| 231 | AM | Armenia | Y60-79 | From 60 to 79 years | 119 | 77 | 491 | 270 | 221 | 273 | 215 | 266 | 268 | 244 | 271 | 263 | 297 | 260 | 245 | 279 | 183 | 255 | 242 | 252 | 217 | 198 | 246 | 227 | 211 | 211 | 185 | 198 | 234 | 180 | 197 | 201 | 243 | 219 | 194 | 156 | 195 | 215 | 189 | 172 | 193 | 205 | 242 | 221 | 236 | 247 | 251 | 247 | 214 | 271 | 211 | 279 | 163 | 151 | 423 | 326 | 254 | 278 | 292 | 235 | 236 | 214 | 212 | 228 | 231 | 227 | 210 | 198 | 198 | 232 | 209 | 212 | 216 | 203 | 220 | 218 | 200 | 206 | 212 | 201 | 197 | 220 | 193 | 187 | 186 | 202 | 205 | 191 | 177 | 191 | 188 | 169 | 268 | 215 | 253 | 223 | 221 | 228 | 231 | 245 | 256 | 276 | 288 | 347 | 235 | 97 | 456 | 306 | 295 | 251 | 237 | 253 | 247 | 243 | 254 | 273 | 242 | 232 | 243 | 200 | 234 | 223 | 229 | 191 | 251 | 196 | 206 | 210 | 195 | 199 | 194 | 200 | 215 | 194 | 211 | 216 | 223 | 200 | 195 | 145 | 180 | 184 | 170 | 175 | 175 | 219 | 210 | 190 | 222 | 229 | 193 | 226 | 222 | 222 | 242 | 225 | 255 | 88 | 351 | 221 | 248 | 228 | 224 | 228 | 223 | 202 | 161 | 276 | 199 | 211 | 206 | 203 | 227 | 219 | 191 | 209 | 199 | 178 | 161 | 202 | 199 | 177 | 162 | 217 | 264 | 234 | 171 | 193 | 196 | 187 | 178 | 175 | 169 | 184 | 134 | 183 | 193 | 160 | 236 | 192 | 186 | 202 | 232 | 232 | 226 | 191 | 224 | 205 | 238 | 52 | 224 | 228 | 243 | 229 | 232 | 218 | 237 | 223 | 195 | 227 | 239 | 222 | 210 | 198 | 204 | 180 | 198 | 183 | 216 | 215 | 225 | 194 | 188 | 189 | 166 | 155 | 191 | 183 | 186 | 210 | 178 | 199 | 194 | 168 | 152 | 180 | 184 | 228 | 184 | 183 | 163 | 179 | 184 | 221 | 217 | 197 | 217 | 220 | 228 | 222 | 274 | 117 | 291 | 284 | 231 | 256 | 238 | 246 | 240 | 240 | 221 | 225 | 227 | 144 | 179 | 202 | 221 | 189 | 212 | 194 | 237 | 216 | 153 | 249 | 256 | 281 | 283 | 273 | 282 | 246 | 260 | 203 | 217 | 201 | 200 | 203 | 204 | 199 | 209 | 203 | 165 | 209 | 233 | 339 | 407 | 512 | 533 | 556 | 552 | 478 | 428 | 372 | 382 | 254 | 212 | 482 | 307 | 290 | 269 | 257 | 257 | 217 | 257 | 221 | 283 | 310 | 338 | 320 | 360 | 333 | 378 | 285 | 261 | 239 | 221 | 210 | 234 | 206 | 219 | 245 | 211 | 211 | 203 | 213 | 186 | 227 | 231 | 226 | 235 | 235 | 264 | 289 | 270 | 289 | 373 | 396 | 459 | 550 | 495 | 492 | 388 | 341 | 332 | 321 | 285 | 192 | 235 | 340 | 265 | 243 | 289 | 276 | 337 | 316 | 288 | 233 | 246 | 198 | 213 | 193 | 202 | 178 | 233 | 214 | 183 | 205 | 221 | 179 | 192 | 217 | 190 | 146 | 147 | 189 | 191 | 214 | 173 | 182 | 179 | 199 | 200 | 217 | 195 | 175 | 209 | 183 | 172 | 171 | 207 | 212 | 205 | 223 | 208 | 202 | 217 | 234 | 231 | 200 | 220 | 291 | 255 | 250 | 213 | 224 | 233 | 239 | 206 | 189 | 212 | 214 | 235 | 211 | 197 | 210 | 206 | 209 | 198 | 193 | 183 | 189 | 196 | 198 | 194 | 186 | 191 | 197 | 198 | 181 | 208 | 217 | 242 | 191 | 203 | 202 | 160 | 176 | 202 | 200 | 215 | 223 | 194 | 213 | 246 | 235 | 213 | 214 | 216 | 239 | 237 | 244 | 214 | 309 | 268 | 264 | 234 | 256 | 268 | 255 | 269 | 222 | 244 | 233 | 219 | None | None | None | None | 215 | 229 | 202 | 255 | 237 | 221 | 238 | 216 | 196 | 188 | 214 | 222 | 199 | 208 | 204 | 210 | 205 | 226 | 215 | 219 | 215 | 207 | 227 | 220 | 234 | 244 | 231 | 230 | 274 | 214 | 219 | 263 | 229 | 245 | 256 | None | None | None | None | None | None |
| 232 | AM | Armenia | Y_GE80 | 80 years or over | 83 | 66 | 389 | 266 | 214 | 219 | 190 | 226 | 244 | 201 | 215 | 233 | 260 | 218 | 242 | 213 | 144 | 219 | 211 | 209 | 184 | 196 | 198 | 177 | 186 | 203 | 190 | 186 | 184 | 156 | 175 | 182 | 174 | 203 | 178 | 125 | 153 | 164 | 164 | 177 | 145 | 146 | 202 | 184 | 172 | 184 | 190 | 226 | 179 | 220 | 238 | 241 | 146 | 124 | 376 | 291 | 211 | 251 | 236 | 237 | 204 | 164 | 215 | 195 | 199 | 195 | 187 | 223 | 209 | 193 | 206 | 193 | 190 | 187 | 213 | 194 | 186 | 198 | 189 | 174 | 162 | 212 | 195 | 148 | 179 | 204 | 155 | 170 | 160 | 142 | 163 | 163 | 177 | 172 | 196 | 202 | 178 | 205 | 242 | 222 | 236 | 283 | 285 | 330 | 278 | 84 | 464 | 304 | 277 | 255 | 275 | 236 | 262 | 230 | 202 | 230 | 252 | 223 | 229 | 272 | 213 | 193 | 247 | 168 | 212 | 186 | 195 | 207 | 197 | 187 | 179 | 183 | 183 | 175 | 196 | 188 | 230 | 214 | 180 | 165 | 147 | 153 | 137 | 153 | 147 | 173 | 191 | 223 | 217 | 221 | 214 | 187 | 214 | 211 | 215 | 222 | 223 | 64 | 356 | 278 | 256 | 222 | 253 | 243 | 248 | 192 | 182 | 260 | 207 | 200 | 157 | 158 | 173 | 188 | 203 | 196 | 179 | 179 | 180 | 177 | 178 | 180 | 198 | 244 | 253 | 236 | 160 | 167 | 187 | 159 | 160 | 147 | 177 | 153 | 141 | 167 | 179 | 122 | 205 | 211 | 199 | 185 | 223 | 222 | 227 | 216 | 204 | 189 | 205 | 52 | 215 | 273 | 265 | 244 | 237 | 243 | 210 | 220 | 227 | 261 | 231 | 242 | 199 | 236 | 190 | 197 | 212 | 237 | 253 | 211 | 175 | 211 | 185 | 190 | 237 | 147 | 223 | 196 | 197 | 186 | 176 | 172 | 195 | 184 | 142 | 149 | 167 | 195 | 184 | 193 | 172 | 205 | 192 | 210 | 192 | 211 | 241 | 209 | 265 | 191 | 262 | 90 | 291 | 256 | 263 | 219 | 263 | 245 | 243 | 280 | 275 | 251 | 227 | 172 | 203 | 218 | 219 | 200 | 206 | 232 | 224 | 249 | 181 | 299 | 259 | 242 | 263 | 264 | 280 | 209 | 290 | 259 | 199 | 198 | 217 | 172 | 185 | 198 | 164 | 194 | 177 | 200 | 228 | 275 | 366 | 501 | 483 | 540 | 515 | 450 | 401 | 388 | 384 | 270 | 194 | 503 | 290 | 268 | 265 | 281 | 260 | 215 | 247 | 209 | 241 | 254 | 279 | 292 | 301 | 328 | 318 | 267 | 265 | 239 | 217 | 237 | 212 | 211 | 216 | 277 | 213 | 195 | 190 | 178 | 162 | 202 | 198 | 221 | 231 | 210 | 228 | 239 | 243 | 269 | 313 | 341 | 369 | 433 | 445 | 425 | 384 | 334 | 297 | 290 | 272 | 227 | 270 | 352 | 284 | 241 | 315 | 352 | 433 | 493 | 396 | 301 | 269 | 243 | 218 | 235 | 176 | 163 | 206 | 186 | 162 | 203 | 184 | 173 | 190 | 184 | 171 | 138 | 146 | 196 | 201 | 211 | 159 | 181 | 174 | 188 | 171 | 178 | 161 | 165 | 210 | 159 | 174 | 164 | 154 | 183 | 177 | 176 | 182 | 173 | 172 | 203 | 184 | 222 | 183 | 261 | 219 | 227 | 220 | 195 | 201 | 203 | 213 | 216 | 227 | 214 | 200 | 176 | 190 | 183 | 171 | 168 | 176 | 177 | 158 | 154 | 142 | 155 | 141 | 154 | 136 | 144 | 155 | 152 | 161 | 165 | 200 | 184 | 154 | 148 | 129 | 124 | 164 | 156 | 185 | 151 | 198 | 175 | 182 | 207 | 181 | 169 | 176 | 205 | 172 | 217 | 167 | 258 | 221 | 220 | 235 | 223 | 220 | 204 | 208 | 197 | 244 | 186 | 180 | None | None | None | None | 197 | 163 | 134 | 191 | 154 | 164 | 187 | 177 | 185 | 137 | 164 | 154 | 157 | 148 | 181 | 173 | 192 | 165 | 159 | 165 | 145 | 148 | 174 | 162 | 161 | 184 | 175 | 191 | 180 | 217 | 186 | 191 | 183 | 214 | 206 | None | None | None | None | None | None |
| 233 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 234 | Special value | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 235 | None | not available | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 236 | Observation flags: | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 237 | p | provisional | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
# This code processes the raw death data (dd) by restructuring it into a long-form dataframe.
# Each row in the new dataframe represents a single country's deaths for a specific year and week,
# along with additional metadata such as country abbreviations and derived values.
temp = dd.melt(id_vars=['name','abbr','agegrp','agegrp_desc'],var_name='year-week',value_name='deaths')
temp['year'] = pd.to_numeric(temp['year-week'].str[0:4])
temp['week'] = pd.to_numeric(temp['year-week'].str[6:8])
temp['year.week'] = temp['year'] + temp['week']/100
temp['year.p'] = temp['year'] + (temp['week']/53.001)
dd = temp
# lets remove some data we don't need
# this is a combination of 27 countries
dd = dd[dd['abbr']!= 'EU27_2020']
dd = dd[dd['abbr']!= 'not available']
dd = dd[dd['abbr']!= 'Special value']
dd = dd[dd['abbr']!= 'None']
dd = dd[dd['abbr']!= 'Observation flags:']
dd = dd[dd['abbr']!= 'p']
# too early for 2025
dd = dd[dd['year']!= 2025]
dd = dd[~dd['name'].isna()]
dd = dd[~dd['agegrp'].isna()]
# we don't need these columns
dd.drop(columns=['agegrp_desc'], inplace=True)
dd.drop(columns=['year-week'], inplace=True)
# converting columns
dd['deaths'] = pd.to_numeric(dd['deaths'])
# there are quite a few NAN (not a number) values
# here we get rid of them
# Get counts of NA values for each 'abbr' group
na_counts = dd[dd.deaths.isna()].groupby(['abbr','agegrp']).size()
# Filter for 'abbr' groups with more than 12 NA values
filtered_abbrs = na_counts[na_counts > 12*5].index
for abbr, agegrp in filtered_abbrs:
print(f'removing -- {abbr} {abbr_to_name(abbr)} {agegrp} NACount={na_counts[(abbr, agegrp)]}')
# Corrected filtering condition: remove rows where abbr and agegrp match separately
dd = dd[~((dd.abbr == abbr) & (dd.agegrp == agegrp))] # Use bitwise AND & inside the negation
# print(len(dd))
removing -- AD Andorra TOTAL NACount=261 removing -- AD Andorra Y20-39 NACount=261 removing -- AD Andorra Y40-59 NACount=261 removing -- AD Andorra Y60-79 NACount=261 removing -- AD Andorra Y_GE80 NACount=261 removing -- AD Andorra Y_LT20 NACount=261 removing -- AL Albania TOTAL NACount=171 removing -- AL Albania Y20-39 NACount=171 removing -- AL Albania Y40-59 NACount=171 removing -- AL Albania Y60-79 NACount=171 removing -- AL Albania Y_GE80 NACount=171 removing -- AL Albania Y_LT20 NACount=171 removing -- GE Georgia TOTAL NACount=208 removing -- GE Georgia Y20-39 NACount=208 removing -- GE Georgia Y40-59 NACount=208 removing -- GE Georgia Y60-79 NACount=208 removing -- GE Georgia Y_GE80 NACount=208 removing -- GE Georgia Y_LT20 NACount=208 removing -- IE Ireland Y20-39 NACount=522
removing -- IE Ireland Y40-59 NACount=522 removing -- IE Ireland Y60-79 NACount=522 removing -- IE Ireland Y_GE80 NACount=522 removing -- IE Ireland Y_LT20 NACount=522 removing -- UK United Kingdom TOTAL NACount=210 removing -- UK United Kingdom Y20-39 NACount=210 removing -- UK United Kingdom Y40-59 NACount=211 removing -- UK United Kingdom Y60-79 NACount=210 removing -- UK United Kingdom Y_GE80 NACount=210 removing -- UK United Kingdom Y_LT20 NACount=210
# Fill missing values (NaNs) in the 2024 data with the mean deaths for 2024 by country
# Filter data for the year 2024
df_2024 = dd[dd.year == 2024]
# Group the 2024 data by country abbreviation ('abbr') and calculate summary statistics
grouped = df_2024.groupby(['abbr','agegrp'])
# Compute statistics: mean, total count, number of NaNs, total deaths, and mean deaths per week
stats = grouped['deaths'].agg(
Mean='mean', # Average deaths for each country in 2024
Count='size', # Total number of records for each country in 2024
NACount=lambda x: x.isna().sum(), # Count of missing (NaN) values
SumFor2024='sum' # Total deaths for each country in 2024
).reset_index()
# Replace missing death values in 2024 with the calculated mean for each country
for _, row in stats.iterrows():
dd.loc[
(dd.abbr == row['abbr']) & (dd.agegrp == row['agegrp']) & (dd.year == 2024) & (dd.deaths.isna()),
'deaths'
] = row['Mean']
# we are going to create a new age group, everyone less than 80
# LT80 is less than 80 years old
# exclude where the age is greater than 80
temp = dd.copy()
temp = temp[temp['agegrp'] != 'TOTAL']
temp = temp[temp['agegrp'] != 'Y_GE80']
temp = pd.pivot_table(
temp,
values='deaths',
index=['name', 'abbr','year','week','year.week','year.p'],
aggfunc='sum'
)
temp = temp.reset_index()
temp['agegrp'] = 'LT80'
dd = pd.concat([dd,temp])
# checking the data
display(dd.head(5))
display(dd.tail(5))
| name | abbr | agegrp | deaths | year | week | year.week | year.p | |
|---|---|---|---|---|---|---|---|---|
| 5 | Belgium | BE | TOTAL | 2461.000 | 2015 | 1 | 2015.010 | 2015.019 |
| 6 | Belgium | BE | Y_LT20 | 19.000 | 2015 | 1 | 2015.010 | 2015.019 |
| 7 | Belgium | BE | Y20-39 | 34.000 | 2015 | 1 | 2015.010 | 2015.019 |
| 8 | Belgium | BE | Y40-59 | 191.000 | 2015 | 1 | 2015.010 | 2015.019 |
| 9 | Belgium | BE | Y60-79 | 756.000 | 2015 | 1 | 2015.010 | 2015.019 |
| name | abbr | agegrp | deaths | year | week | year.week | year.p | |
|---|---|---|---|---|---|---|---|---|
| 17221 | Switzerland | CH | LT80 | 420.000 | 2024 | 48 | 2024.480 | 2024.906 |
| 17222 | Switzerland | CH | LT80 | 414.000 | 2024 | 49 | 2024.490 | 2024.925 |
| 17223 | Switzerland | CH | LT80 | 466.000 | 2024 | 50 | 2024.500 | 2024.943 |
| 17224 | Switzerland | CH | LT80 | 472.000 | 2024 | 51 | 2024.510 | 2024.962 |
| 17225 | Switzerland | CH | LT80 | 501.000 | 2024 | 52 | 2024.520 | 2024.981 |
# save to out folder
dd.to_csv(r'out\death_data.csv',index=False)
ddy = pd.pivot_table(
dd,
values='deaths',
index=['name', 'abbr','agegrp','year'],
aggfunc='sum'
)
ddy = ddy.reset_index()
display(ddy.head(5))
display(ddy.tail(5))
# save to out folder
ddy.to_csv(r'out\death_data_year.csv',index=False)
| name | abbr | agegrp | year | deaths | |
|---|---|---|---|---|---|
| 0 | Armenia | AM | LT80 | 2015 | 17785.000 |
| 1 | Armenia | AM | LT80 | 2016 | 17334.000 |
| 2 | Armenia | AM | LT80 | 2017 | 16426.000 |
| 3 | Armenia | AM | LT80 | 2018 | 15379.000 |
| 4 | Armenia | AM | LT80 | 2019 | 14880.000 |
| name | abbr | agegrp | year | deaths | |
|---|---|---|---|---|---|
| 2315 | Switzerland | CH | Y_LT20 | 2020 | 491.000 |
| 2316 | Switzerland | CH | Y_LT20 | 2021 | 468.000 |
| 2317 | Switzerland | CH | Y_LT20 | 2022 | 511.000 |
| 2318 | Switzerland | CH | Y_LT20 | 2023 | 451.000 |
| 2319 | Switzerland | CH | Y_LT20 | 2024 | 399.000 |
# here we normalize the death values
ddn = ddy.copy()
ddn['deaths_norm'] = 0.0
## baseline years are 2015,2016,2017 before the pandemic
blyears = ddn[ddn.year.isin([2015,2016,2017])]
grouped = blyears.groupby(['abbr','agegrp'])
# Compute baseline mean
temp = grouped['deaths'].agg(
baseline='mean',
).reset_index()
# Merge baseline means with the original DataFrame
ddn = ddn.merge(temp, on=['abbr', 'agegrp'], how='left')
# Normalize deaths column
ddn['deaths_norm'] = ddn['deaths'] / ddn['baseline']
# Drop the intermediate baseline column if not needed
ddn.drop(columns=['baseline'], inplace=True)
# save to out folder
ddn.to_csv(r'out\death_data_norm.csv',index=False)
cause of death¶
Getting the Data¶
- go to Europa.eu - Database
- choose
- Population and social conditions
- Health
- Causes of death
- General mortality
- Causes of death - deaths by country of residence and occurrence
- Click the little table
- customize the data
- Customize your dataset -> Time -> From - to
- From: 2015
- To: [Current or Max]
- Customize your dataset ->
International Statistical Classification of Diseases and Related Health Problems (ICD-10 2010)- All
- UnCheck All
- Level 1
- All Checked
Level 1 is basic classification of the cause of death
- All
- Move the ``International Statistical Classification...
underGeopolitical entity (reporting)`
- Customize your dataset -> Time -> From - to
- Click
download(as a spreadsheet) and place the file in the.\datafolder


variables¶
- cod = cause of death data
- codn = cod, normalized
# getting the data
# Level 1
cod = pd.read_excel(os.path.join(DataDIR,"hlth_cd_aro__custom_15699666_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")
# Aggregate
# cod = pd.read_excel(os.path.join(DataDIR,"hlth_cd_aro__custom_15989358_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")
# remove the headers
cod = cod.iloc[9::]
# drop the bad columns
for c in cod.columns:
if pd.isnull(cod.at[9,c]):
cod = cod.drop(columns=[c])
# rename time columns
for c in cod.columns:
name = cod.at[9,c]
cod = cod.rename(columns={c: name})
# make the duplicate column names unique
cod = df_column_uniquify(cod)
# # rename the first two columns
cod = cod.rename(columns={'TIME': 'name'})
cod = cod.rename(columns={'TIME_1':'cod'})
# drop, replace, reset index,
cod = cod.drop([9,10])
cod = cod.replace(to_replace=':', value=None)
cod = cod.reset_index(drop=True)
cod['abbr'] = cod['name'].apply(name_to_abbr)
# display(cod.head(5))
C:\Users\JGarza\pythons\Python312\Lib\site-packages\openpyxl\styles\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default
warn("Workbook contains no default style, apply openpyxl's default")
# This code processes the raw cod data (cod) by restructuring it into a long-form dataframe.
# along with additional metadata such as country abbreviations and derived values.
temp = cod.melt(id_vars=['name','abbr','cod'],var_name='year',value_name='deaths')
temp['year'] = pd.to_numeric(temp['year'])
cod = temp
# display(cod.head(10))
# lets remove some data we don't need
# this is a combination of 27 countries
cod.loc[cod['name']== 'European Union - 27 countries (from 2020)','abbr'] = 'Euro27'
cod = cod[cod['name']!= 'not available']
cod = cod[cod['name']!= 'Special value']
cod = cod[cod['name']!= 'None']
cod = cod[cod['name']!= 'Observation flags:']
cod = cod[cod['name']!= 'p']
cod = cod[cod['name']!= 'Nan']
cod = cod[cod['name']!= 'd']
cod = cod[~cod['name'].isna()]
# removed due to lack of reporting data
cod = cod[cod['abbr']!= 'Euro27']
cod = cod[cod['abbr']!= 'Unknown']
cod = cod[cod['abbr']!= 'UK']
cod = cod[cod['abbr']!= 'LI']
# display(cod.tail(10))
# if a cause of death is null, we'll fill it with 0
# cod['deaths'].fillna(0, inplace=True) #deprecated
cod['deaths'] = pd.to_numeric(cod['deaths'], errors='coerce').fillna(0).astype(int)
# save to out folder
cod.to_csv(r'out\cod_data.csv',index=False)
# here we normalize the cod data
codn = cod.copy()
codn['deaths_norm'] = np.nan
## baseline years are 2015,2016,2017 before the pandemic
blyears = codn[codn.year.isin([2015,2016,2017])]
grouped = blyears.groupby(['name','cod'])
# Compute baseline mean
temp = grouped['deaths'].agg(
baseline='mean',
).reset_index()
# Merge baseline means with the original DataFrame
codn = codn.merge(temp, on=['name', 'cod'], how='left')
codn['baseline'] = codn['baseline'].replace(0, np.nan)
# set the death_norm to deaths/balseline
# * if the baseline is not NA
# * if the deaths is not NA
codn.loc[(~codn['baseline'].isna()) & (~codn['deaths'].isna()), 'deaths_norm'] = codn['deaths'] / codn['baseline']
# Drop the intermediate baseline column if not needed
codn.drop(columns=['baseline'], inplace=True)
# # save to out folder
codn.to_csv(r'out\cod_data_norm.csv',index=False)
# display(codn.head(1000))
vaccine data¶
Getting the Data¶
- Go to https://www.ecdc.europa.eu/
- Click the
Download in CSV.
variables¶
- vd = vaccine data
# import Vaccine Data
vd = pd.read_csv(os.path.join(DataDIR,'data.csv'))
vd = vd[vd['TargetGroup'] == 'ALL']
# create a year column
vd['year'] = pd.to_numeric(vd['YearWeekISO'].str[0:4])
# renaming columns for shorter names
vd = vd.rename(columns={'ReportingCountry':'abbr'})
vd = vd.rename(columns={'Vaccine':'vacc'})
vd = vd.rename(columns={'FirstDose':'dose1'})
vd = vd.rename(columns={'SecondDose':'dose2'})
vd = vd.rename(columns={'DoseAdditional1':'doesA1'})
vd = vd.rename(columns={'DoseAdditional2':'doesA2'})
vd = vd.rename(columns={'DoseAdditional3':'doesA3'})
vd = vd.rename(columns={'DoseAdditional4':'doesA4'})
vd = vd.rename(columns={'DoseAdditional5':'doesA5'})
vd = vd.rename(columns={'UnknownDose':'doseUNK'})
doseCol = ['dose1','dose2','doesA1','doesA2','doesA3','doesA4','doesA5','doseUNK']
# calculate the sum of all the Doses
vd = pd.pivot_table(
data = vd,
values = doseCol,
index = ['abbr','year','Population','vacc'],
aggfunc="sum"
)
vd = vd.reset_index()
# gets get the total doses given
vd['total_dose'] = vd[doseCol].sum(axis=1)
doseCol.append('total_dose')
# lets make a new record for all vaccines
temp = pd.pivot_table(
data = vd,
values = doseCol,
index = ['abbr','year','Population'],
aggfunc="sum"
)
temp = temp.reset_index()
temp['vacc'] = 'All'
# and add it to all the vaccine data
vd = pd.concat([vd,temp])
# gets get the total dose1 (first dose) given
vd['total_dose1'] = vd['dose1']
# dose1 ... cumulative sum, dose1/pop. , dose1/pop. normalize
vd['td1_sum'] = vd.groupby(['abbr','vacc'])['total_dose1'].cumsum()
vd['dose1_pop_ratio'] = vd['td1_sum']/vd['Population']
## normalizsed version
vd['dpr1_norm'] = (vd['dose1_pop_ratio'] - vd['dose1_pop_ratio'].min()) / (vd['dose1_pop_ratio'].max() - vd['dose1_pop_ratio'].min())
vd['td_sum'] = vd.groupby(['abbr','vacc'])['total_dose'].cumsum()
vd['dose_pop_ratio'] = vd['td_sum']/vd['Population']
## normalizsed version
vd['dpr_norm'] = (vd['dose_pop_ratio'] - vd['dose_pop_ratio'].min()) / (vd['dose_pop_ratio'].max() - vd['dose_pop_ratio'].min())
vd['name'] = vd.abbr.apply(abbr_to_name)
# display(vd.head(5))
# display(vd.tail(5))
# save to out folder
vd.to_csv(r'out\vacc_data.csv',index=False)
# display(vd.head(100))
# an example of a few
display(vd[(vd['abbr']=='FI') & (vd['vacc']=='All')])
display(vd[(vd['abbr']=='SK') & (vd['vacc']=='All')])
display(vd[(vd['abbr']=='RO') & (vd['vacc']=='All')])
| abbr | year | Population | vacc | doesA1 | doesA2 | doesA3 | doesA4 | doesA5 | dose1 | dose2 | doseUNK | total_dose | total_dose1 | td1_sum | dose1_pop_ratio | dpr1_norm | td_sum | dose_pop_ratio | dpr_norm | name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 40 | FI | 2020 | 5525292 | All | 0 | 0 | 0 | 0 | 0 | 17151 | 0 | 0 | 17151 | 17151 | 17151 | 0.003 | 0.001 | 17151 | 0.003 | 0.000 | Finland |
| 41 | FI | 2021 | 5533793 | All | 3835791 | 2706 | 0 | 0 | 0 | 12062376 | 11587069 | 0 | 27487942 | 12062376 | 12079527 | 2.183 | 0.990 | 27505093 | 4.970 | 0.711 | Finland |
| 42 | FI | 2022 | 5548241 | All | 5333235 | 3641874 | 1426157 | 0 | 0 | 153691 | 359625 | 0 | 10914582 | 153691 | 12233218 | 2.205 | 1.000 | 38419675 | 6.925 | 0.990 | Finland |
| 43 | FI | 2023 | 5548241 | All | 21628 | 143580 | 209551 | 0 | 0 | 3285 | 4192 | 0 | 382236 | 3285 | 12236503 | 2.205 | 1.000 | 38801911 | 6.994 | 1.000 | Finland |
| abbr | year | Population | vacc | doesA1 | doesA2 | doesA3 | doesA4 | doesA5 | dose1 | dose2 | doseUNK | total_dose | total_dose1 | td1_sum | dose1_pop_ratio | dpr1_norm | td_sum | dose_pop_ratio | dpr_norm | name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 114 | SK | 2020 | 5457873 | All | 0 | 0 | 0 | 0 | 0 | 4536 | 5 | 0 | 4541 | 4536 | 4536 | 0.001 | 0.000 | 4541 | 0.001 | 0.000 | Slovakia |
| 115 | SK | 2021 | 5459781 | All | 1103260 | 5 | 0 | 0 | 0 | 2654986 | 2400025 | 0 | 6158276 | 2654986 | 2659522 | 0.487 | 0.221 | 6162817 | 1.129 | 0.161 | Slovakia |
| 116 | SK | 2022 | 5434712 | All | 573964 | 73532 | 669 | 0 | 0 | 45368 | 92061 | 0 | 785594 | 45368 | 2704890 | 0.498 | 0.226 | 6948411 | 1.279 | 0.183 | Slovakia |
| 117 | SK | 2023 | 5434712 | All | 634 | 4719 | 486 | 0 | 0 | 367 | 296 | 0 | 6502 | 367 | 2705257 | 0.498 | 0.226 | 6954913 | 1.280 | 0.183 | Slovakia |
| abbr | year | Population | vacc | doesA1 | doesA2 | doesA3 | doesA4 | doesA5 | dose1 | dose2 | doseUNK | total_dose | total_dose1 | td1_sum | dose1_pop_ratio | dpr1_norm | td_sum | dose_pop_ratio | dpr_norm | name | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 103 | RO | 2021 | 19201662 | All | 1140530 | 0 | 0 | 0 | 0 | 7734378 | 5677069 | 0 | 14551977 | 7734378 | 7734378 | 0.403 | 0.183 | 14551977 | 0.758 | 0.108 | Romania |
| 104 | RO | 2022 | 19042455 | All | 611197 | 25319 | 0 | 0 | 0 | 181348 | 184394 | 0 | 1002258 | 181348 | 7915726 | 0.416 | 0.188 | 15554235 | 0.817 | 0.117 | Romania |
| 105 | RO | 2023 | 19042455 | All | 2714 | 6420 | 0 | 0 | 0 | 2219 | 1678 | 0 | 13031 | 2219 | 7917945 | 0.416 | 0.189 | 15567266 | 0.818 | 0.117 | Romania |
cols = ['name','abbr', 'year','source', 'filter','value_type','value']
cd = pd.DataFrame(columns=cols)
# deaths data
temp = ddy.copy()
temp['source'] = 'deaths'
temp['filter'] = temp['agegrp']
temp['value_type'] = 'value'
temp['value'] = temp['deaths']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# deaths data normalized
temp = ddn.copy()
temp['source'] = 'deaths'
temp['filter'] = temp['agegrp']
temp['value_type'] = 'normalized'
temp['value'] = temp['deaths_norm']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# cause of death data
temp = cod.copy()
temp['source'] = 'cause_of_death'
temp['filter'] = temp['cod']
temp['value_type'] = 'value'
temp['value'] = temp['deaths']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# cause of death data normalized
temp = codn.copy()
temp['source'] = 'cause_of_death'
temp['filter'] = temp['cod']
temp['value_type'] = 'normalized'
temp['value'] = temp['deaths_norm']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - total_dose1
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'value (first dose)'
temp['value'] = temp['total_dose1']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - total_dose1/pop
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'ratio (first dose/pop.)'
temp['value'] = temp['dose1_pop_ratio']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - total_dose1/pop normalizsed
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'normalized (first dose/pop.)'
temp['value'] = temp['dpr1_norm']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - total_dose
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'value (total_dose)'
temp['value'] = temp['total_dose']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - dose_pop_ratio
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'ratio (total_dose/pop.)'
temp['value'] = temp['dose_pop_ratio']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
# Vaccine Data - dpr_norm
temp = vd.copy()
temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'normalized (total_dose/pop.)'
temp['value'] = temp['dpr_norm']
for c in temp.columns:
if c not in cols:
temp = temp.drop(columns=[c])
cd = pd.concat([cd,temp])
display(cd.tail(5))
display(cd[(cd['source'] == 'deaths') & (cd['value_type'] == 'normalized')].tail(5))
# save to out folder
cd.to_csv(r'out\combined_data.csv',index=False)
C:\Users\JGarza\AppData\Local\Temp\ipykernel_27628\3464685614.py:18: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. cd = pd.concat([cd,temp])
| name | abbr | year | source | filter | value_type | value | |
|---|---|---|---|---|---|---|---|
| 113 | Slovenia | SI | 2023 | vaccine | All | normalized (total_dose/pop.) | 0.197 |
| 114 | Slovakia | SK | 2020 | vaccine | All | normalized (total_dose/pop.) | 0.000 |
| 115 | Slovakia | SK | 2021 | vaccine | All | normalized (total_dose/pop.) | 0.161 |
| 116 | Slovakia | SK | 2022 | vaccine | All | normalized (total_dose/pop.) | 0.183 |
| 117 | Slovakia | SK | 2023 | vaccine | All | normalized (total_dose/pop.) | 0.183 |
| name | abbr | year | source | filter | value_type | value | |
|---|---|---|---|---|---|---|---|
| 2315 | Switzerland | CH | 2020 | deaths | Y_LT20 | normalized | 0.974 |
| 2316 | Switzerland | CH | 2021 | deaths | Y_LT20 | normalized | 0.929 |
| 2317 | Switzerland | CH | 2022 | deaths | Y_LT20 | normalized | 1.014 |
| 2318 | Switzerland | CH | 2023 | deaths | Y_LT20 | normalized | 0.895 |
| 2319 | Switzerland | CH | 2024 | deaths | Y_LT20 | normalized | 0.792 |
sources = cd['source'].drop_duplicates().to_list()
for s in sources:
print('source: ',s)
filters = cd[cd['source']==s]['filter'].drop_duplicates().to_list()
print('filter:')
print('\t',filters)
value_types = cd[cd['source']==s]['value_type'].drop_duplicates().to_list()
print('value_type:')
print('\t',value_types)
print('---')
source: deaths filter: ['LT80', 'TOTAL', 'Y20-39', 'Y40-59', 'Y60-79', 'Y_GE80', 'Y_LT20'] value_type: ['value', 'normalized'] --- source: cause_of_death filter: ['Malignant neoplasms (C00-C97)', 'Endocrine, nutritional and metabolic diseases (E00-E90)', 'Mental and behavioural disorders (F00-F99)', 'Diseases of the circulatory system (I00-I99)', 'Diseases of the respiratory system (J00-J99)', 'Diseases of the digestive system (K00-K93)', 'Diseases of the skin and subcutaneous tissue (L00-L99)', 'Diseases of the musculoskeletal system and connective tissue (M00-M99)', 'Diseases of the genitourinary system (N00-N99)', 'Pregnancy, childbirth and the puerperium (O00-O99)', 'Certain conditions originating in the perinatal period (P00-P96)', 'Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)', 'Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)'] value_type: ['value', 'normalized'] --- source: vaccine filter: ['AZ', 'COM', 'COMBA.1', 'COMBA.4-5', 'JANSS', 'MOD', 'MODBA.1', 'NVXD', 'SGSK', 'UNK', 'VLA', 'MODBA.4-5', 'COMXBB', 'COMBIV', 'MODBIV', 'BECNBG', 'SPU', 'SIN', 'BHACOV', 'All'] value_type: ['value (first dose)', 'ratio (first dose/pop.)', 'normalized (first dose/pop.)', 'value (total_dose)', 'ratio (total_dose/pop.)', 'normalized (total_dose/pop.)'] ---
Lets Visualize the Data¶
Line Charts - normalized deaths¶
- Note the chart below shows that all the countries went through the pandemic 2020-2021, however some countries recovered (1.0 or less), and other countries still have higher than normal death rates (over 1.0).
title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))
temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]
temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)
fig = px.line(
temp,
x='year',
y='value',
color='name',
height=750 ,
hover_data={
'name', 'abbr', 'value'
},
title=title
)
fig.update_layout(template="plotly_dark")
title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))
subtitle = 'Only the top 5 values in 2024, and the bottom 5 values in 2024'
display(MD(f'#### {subtitle}'))
temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]
head2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).head(5)
tail2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).tail(5)
temp = temp[
(temp['name'].isin(head2024.name))
| (temp['name'].isin(tail2024.name))
]
temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)
fig = px.line(
temp,
x='year',
y='value',
color='name',
height=750 ,
hover_data={
'name', 'abbr', 'value'
},
title=title
)
fig.update_layout(template="plotly_dark")
- Note
here we can see some data might not be trust-worthy.
see
Liechtenstein
title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))
display(MD(f'#### Only the top 5 values in 2024, and the bottom 5 values in 2024'))
display(MD(f'#### Only where the deaths are from people who are less than 80 years old'))
temp = cd[(cd['source']=='deaths') & (cd['filter']=='LT80') & (cd['value_type']=='normalized')]
head2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).head(5)
tail2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).tail(5)
temp = temp[
(temp['name'].isin(head2024.name))
| (temp['name'].isin(tail2024.name))
]
temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)
fig = px.line(
temp,
x='year',
y='value',
color='name',
height=750 ,
hover_data={
'name', 'abbr', 'value'
},
title=title
)
fig.update_layout(template="plotly_dark")
HeatMaps of deaths by AgeGroups¶
display(MD('### HeatMap (of deaths based on age groups)'))
agegrps = cd[(cd['source']=='deaths')]['filter'].drop_duplicates().to_list()
for ag in agegrps:
display(MD(f'#### {ag}'))
temp = cd[(cd['source']=='deaths') & (cd['filter']==ag) & (cd['value_type']=='normalized')]
temp = pd.pivot_table(
data = temp,
values = 'value',
index = ['abbr','name','filter','value_type'],
columns=['year'],
aggfunc='mean',
)
temp.columns.name = 'index'
temp = temp.reset_index()
## sum of post pandemic deaths
sppd = pd.pivot_table(
cd[(cd['source']=='deaths') & (cd['filter']==ag) & (cd['value_type']=='normalized') & (cd['year'] > 2023)], # totals only
values = 'value',
index = ['abbr','name'],
aggfunc='sum',
)
sppd = sppd.reset_index()
sppd = sppd.sort_values(by='value',ascending=False)
temp = temp.set_index('name').reindex(sppd['name']).reset_index()
display(temp.style.background_gradient(cmap=heatmapCM,axis=1))
HeatMap (of deaths based on age groups)¶
LT80¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Iceland | IS | LT80 | normalized | 0.963504 | 1.036496 | 1.000000 | 1.019812 | 1.012513 | 1.105318 | 1.123045 | 1.273201 | 1.211679 | 1.234619 |
| 1 | Malta | MT | LT80 | normalized | 1.017293 | 0.967203 | 1.015504 | 1.057841 | 1.036971 | 1.166369 | 1.196780 | 1.207513 | 1.124031 | 1.163387 |
| 2 | Netherlands | NL | LT80 | normalized | 1.009040 | 0.998123 | 0.992837 | 1.013964 | 1.004492 | 1.114691 | 1.143528 | 1.122958 | 1.113380 | 1.110753 |
| 3 | Norway | NO | LT80 | normalized | 1.003819 | 0.998207 | 0.997974 | 1.013113 | 1.015627 | 1.033864 | 1.050348 | 1.137911 | 1.107398 | 1.106405 |
| 4 | Liechtenstein | LI | LT80 | normalized | 0.876923 | 1.107692 | 1.015385 | 1.007692 | 0.946154 | 1.115385 | 0.976923 | 0.000000 | 0.953846 | 1.092308 |
| 5 | France | FR | LT80 | normalized | 1.012897 | 0.989268 | 0.997835 | 1.006304 | 1.003646 | 1.094593 | 1.112343 | 1.124722 | 1.086984 | 1.085468 |
| 6 | Cyprus | CY | LT80 | normalized | 1.026756 | 0.958661 | 1.014583 | 1.020289 | 1.053005 | 1.140122 | 1.280878 | 1.213923 | 1.143546 | 1.070885 |
| 7 | Spain | ES | LT80 | normalized | 1.029906 | 0.983827 | 0.986268 | 0.997999 | 0.994437 | 1.164643 | 1.109420 | 1.114142 | 1.082376 | 1.068357 |
| 8 | Finland | FI | LT80 | normalized | 1.000246 | 1.000328 | 0.999426 | 0.998402 | 0.988075 | 1.026145 | 1.040611 | 1.090771 | 1.100033 | 1.050775 |
| 9 | Luxembourg | LU | LT80 | normalized | 0.979849 | 0.945681 | 1.074470 | 1.028211 | 1.006658 | 1.077098 | 1.061854 | 1.024006 | 1.026108 | 1.037147 |
| 10 | Poland | PL | LT80 | normalized | 1.017801 | 0.981581 | 1.000618 | 1.023203 | 1.018394 | 1.181092 | 1.309963 | 1.115682 | 1.035903 | 1.026510 |
| 11 | Belgium | BE | LT80 | normalized | 1.029200 | 0.986823 | 0.983978 | 0.983892 | 0.964447 | 1.091920 | 1.040751 | 1.042612 | 1.006225 | 1.008963 |
| 12 | Austria | AT | LT80 | normalized | 1.016921 | 0.987902 | 0.995178 | 1.019837 | 1.002171 | 1.082151 | 1.076318 | 1.062049 | 1.020941 | 1.007465 |
| 13 | Slovakia | SK | LT80 | normalized | 1.024468 | 0.976413 | 0.999120 | 1.017851 | 1.008905 | 1.130455 | 1.421736 | 1.114427 | 1.030183 | 1.002475 |
| 14 | Portugal | PT | LT80 | normalized | 1.014427 | 1.003856 | 0.981716 | 0.995044 | 0.976160 | 1.077223 | 1.079219 | 1.056038 | 1.019550 | 1.000644 |
| 15 | Czechia | CZ | LT80 | normalized | 1.016100 | 0.979352 | 1.004548 | 1.027520 | 1.023851 | 1.174844 | 1.324658 | 1.084517 | 1.020200 | 0.989842 |
| 16 | Greece | EL | LT80 | normalized | 1.027115 | 0.983239 | 0.989646 | 0.954367 | 0.959678 | 1.028211 | 1.152957 | 1.074960 | 1.013372 | 0.989274 |
| 17 | Denmark | DK | LT80 | normalized | 1.010938 | 0.999848 | 0.989214 | 1.020657 | 1.005069 | 1.023020 | 1.039904 | 1.069213 | 1.031748 | 0.983383 |
| 18 | Switzerland | CH | LT80 | normalized | 1.035616 | 0.972509 | 0.991875 | 0.997235 | 0.997587 | 1.061751 | 1.058817 | 1.057173 | 1.011007 | 0.972313 |
| 19 | Slovenia | SI | LT80 | normalized | 1.029287 | 0.970468 | 1.000245 | 1.000559 | 0.999616 | 1.096075 | 1.121134 | 1.044700 | 1.001922 | 0.966484 |
| 20 | Estonia | EE | LT80 | normalized | 1.030864 | 0.996006 | 0.973130 | 0.977366 | 0.941782 | 0.967320 | 1.096466 | 0.995885 | 0.947470 | 0.960300 |
| 21 | Montenegro | ME | LT80 | normalized | 1.014343 | 1.010269 | 0.975388 | 0.978189 | 0.983281 | 1.097853 | 1.373334 | 1.026564 | 0.951201 | 0.959582 |
| 22 | Hungary | HU | LT80 | normalized | 1.022566 | 0.974640 | 1.002795 | 0.997790 | 0.984532 | 1.092329 | 1.230570 | 1.037761 | 0.975407 | 0.954401 |
| 23 | Bulgaria | BG | LT80 | normalized | 1.019357 | 0.986378 | 0.994265 | 0.990131 | 0.985726 | 1.172561 | 1.436468 | 1.093518 | 0.945354 | 0.942555 |
| 24 | Serbia | RS | LT80 | normalized | 1.037063 | 0.975378 | 0.987559 | 0.964466 | 0.969101 | 1.164052 | 1.333456 | 0.998404 | 0.644844 | 0.942320 |
| 25 | Armenia | AM | LT80 | normalized | 1.035115 | 1.008866 | 0.956019 | 0.895082 | 0.866039 | 1.251799 | 1.237074 | 0.909807 | 0.872500 | 0.926923 |
| 26 | Italy | IT | LT80 | normalized | 1.037174 | 0.970996 | 0.991830 | 0.964604 | 0.945012 | 1.095522 | 1.051458 | 1.004187 | 0.949085 | 0.916106 |
| 27 | Germany | DE | LT80 | normalized | 1.028583 | 0.988456 | 0.982961 | 0.991662 | 0.958617 | 0.982483 | 0.998447 | 1.003770 | 0.977197 | 0.914716 |
| 28 | Sweden | SE | LT80 | normalized | 1.024029 | 0.989863 | 0.986107 | 0.998720 | 0.958976 | 1.056766 | 1.005839 | 0.994011 | 0.979409 | 0.910600 |
| 29 | Croatia | HR | LT80 | normalized | 1.053916 | 0.963024 | 0.983060 | 0.959672 | 0.932277 | 1.039490 | 1.131585 | 0.990455 | 0.927541 | 0.897778 |
| 30 | Lithuania | LT | LT80 | normalized | 1.055938 | 1.008323 | 0.935740 | 0.921503 | 0.892768 | 1.045249 | 1.102589 | 0.960708 | 0.870034 | 0.879276 |
| 31 | Latvia | LV | LT80 | normalized | 1.022160 | 0.991260 | 0.986580 | 0.970320 | 0.912900 | 0.951600 | 1.127640 | 0.966600 | 0.895140 | 0.833580 |
| 32 | Romania | RO | LT80 | normalized | 1.024865 | 0.986505 | 0.988630 | 0.988669 | 0.976968 | 1.130885 | 1.277982 | 0.997127 | 0.911279 | 0.829676 |
TOTAL¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Malta | MT | TOTAL | normalized | 1.016949 | 0.955701 | 1.027350 | 1.060863 | 1.064330 | 1.199538 | 1.196071 | 1.219183 | 1.161980 | 1.183648 |
| 1 | Iceland | IS | TOTAL | normalized | 0.988146 | 1.020151 | 0.991702 | 1.000148 | 1.005927 | 1.042377 | 1.039265 | 1.195733 | 1.140169 | 1.153060 |
| 2 | Netherlands | NL | TOTAL | normalized | 1.003373 | 0.993167 | 1.003460 | 1.024649 | 1.015107 | 1.147749 | 1.140424 | 1.133556 | 1.131250 | 1.143136 |
| 3 | Cyprus | CY | TOTAL | normalized | 1.029186 | 0.937156 | 1.033658 | 0.997362 | 1.069610 | 1.143922 | 1.241800 | 1.239908 | 1.150459 | 1.124312 |
| 4 | Ireland | IE | TOTAL | normalized | 1.007002 | 0.998439 | 0.994559 | 1.013608 | 1.002031 | 1.041412 | 1.098655 | 1.128063 | 1.125593 | 1.117736 |
| 5 | Liechtenstein | LI | TOTAL | normalized | 0.997423 | 1.043814 | 0.958763 | 1.055412 | 1.005155 | 1.256443 | 1.032216 | 1.078608 | 1.020619 | 1.117268 |
| 6 | Luxembourg | LU | TOTAL | normalized | 0.992076 | 0.966098 | 1.041827 | 1.053835 | 1.050895 | 1.143779 | 1.100400 | 1.089127 | 1.081529 | 1.095744 |
| 7 | Germany | DE | TOTAL | normalized | 1.016481 | 0.979311 | 1.004209 | 1.029001 | 1.012227 | 1.082065 | 1.101127 | 1.149954 | 1.106922 | 1.079127 |
| 8 | Finland | FI | TOTAL | normalized | 0.996378 | 1.003504 | 1.000118 | 1.016952 | 1.004682 | 1.051873 | 1.076956 | 1.155757 | 1.143393 | 1.078209 |
| 9 | Norway | NO | TOTAL | normalized | 1.016140 | 0.989495 | 0.994364 | 0.996615 | 0.991477 | 1.008311 | 1.026049 | 1.115550 | 1.065612 | 1.072463 |
| 10 | Portugal | PT | TOTAL | normalized | 1.006119 | 1.000327 | 0.993554 | 1.024547 | 1.013474 | 1.140328 | 1.128946 | 1.127418 | 1.071651 | 1.067896 |
| 11 | Austria | AT | TOTAL | normalized | 1.023795 | 0.972870 | 1.003336 | 1.012615 | 1.006371 | 1.125312 | 1.104853 | 1.130013 | 1.081778 | 1.067871 |
| 12 | Denmark | DK | TOTAL | normalized | 1.007454 | 0.991352 | 1.001194 | 1.038393 | 1.014429 | 1.045972 | 1.075440 | 1.117541 | 1.090769 | 1.064901 |
| 13 | France | FR | TOTAL | normalized | 1.007603 | 0.985137 | 1.007261 | 1.013398 | 1.019252 | 1.131754 | 1.099201 | 1.122302 | 1.061615 | 1.062469 |
| 14 | Slovenia | SI | TOTAL | normalized | 1.005897 | 0.975647 | 1.018456 | 1.018057 | 1.023988 | 1.216257 | 1.150672 | 1.118378 | 1.070087 | 1.061216 |
| 15 | Spain | ES | TOTAL | normalized | 1.023104 | 0.971158 | 1.005738 | 1.013880 | 0.992168 | 1.192895 | 1.070740 | 1.101128 | 1.039692 | 1.042102 |
| 16 | Switzerland | CH | TOTAL | normalized | 1.031079 | 0.969066 | 0.999855 | 1.002628 | 1.012027 | 1.137130 | 1.058255 | 1.110659 | 1.069182 | 1.037839 |
| 17 | Estonia | EE | TOTAL | normalized | 1.003717 | 0.992890 | 1.003393 | 1.016879 | 0.994057 | 1.041062 | 1.194009 | 1.115234 | 1.034320 | 1.030883 |
| 18 | Greece | EL | TOTAL | normalized | 1.012210 | 0.969465 | 1.018325 | 0.984534 | 1.022175 | 1.091374 | 1.179188 | 1.151479 | 1.047889 | 1.026689 |
| 19 | Belgium | BE | TOTAL | normalized | 1.024324 | 0.979883 | 0.995793 | 1.005480 | 0.988120 | 1.170333 | 1.020196 | 1.058305 | 1.012988 | 1.015740 |
| 20 | Poland | PL | TOTAL | normalized | 1.011934 | 0.974416 | 1.013649 | 1.035756 | 1.026469 | 1.222837 | 1.306178 | 1.125759 | 1.027167 | 1.015246 |
| 21 | Czechia | CZ | TOTAL | normalized | 1.024453 | 0.970711 | 1.004837 | 1.019293 | 1.014188 | 1.190285 | 1.259588 | 1.084875 | 1.016894 | 1.003669 |
| 22 | Slovakia | SK | TOTAL | normalized | 1.023092 | 0.973345 | 1.003563 | 1.011692 | 0.992593 | 1.126210 | 1.363524 | 1.109279 | 1.008254 | 0.995303 |
| 23 | Italy | IT | TOTAL | normalized | 1.028396 | 0.960289 | 1.011315 | 0.983592 | 0.989873 | 1.165225 | 1.087305 | 1.095037 | 1.013642 | 0.986208 |
| 24 | Montenegro | ME | TOTAL | normalized | 0.999019 | 0.994684 | 1.006297 | 1.003200 | 1.018684 | 1.138065 | 1.411355 | 1.088052 | 0.977652 | 0.974413 |
| 25 | Sweden | SE | TOTAL | normalized | 1.019326 | 0.985579 | 0.995095 | 0.995218 | 0.956506 | 1.079097 | 0.985779 | 1.011546 | 1.005714 | 0.969874 |
| 26 | Hungary | HU | TOTAL | normalized | 1.026484 | 0.967604 | 1.005912 | 1.000787 | 0.990451 | 1.097414 | 1.187944 | 1.041669 | 0.978737 | 0.968079 |
| 27 | Croatia | HR | TOTAL | normalized | 1.036057 | 0.963041 | 1.000902 | 0.987618 | 0.969749 | 1.088706 | 1.174273 | 1.066778 | 0.959584 | 0.946901 |
| 28 | Serbia | RS | TOTAL | normalized | 1.024670 | 0.974227 | 1.001103 | 0.980439 | 0.979254 | 1.155481 | 1.318381 | 1.013430 | 0.917367 | 0.925909 |
| 29 | Armenia | AM | TOTAL | normalized | 1.008985 | 1.007475 | 0.983540 | 0.920898 | 0.919641 | 1.278886 | 1.266846 | 0.964385 | 0.873495 | 0.916035 |
| 30 | Lithuania | LT | TOTAL | normalized | 1.034162 | 0.993484 | 0.972354 | 0.959272 | 0.928441 | 1.077686 | 1.154059 | 1.030467 | 0.919153 | 0.914776 |
| 31 | Latvia | LV | TOTAL | normalized | 1.009782 | 0.991345 | 0.998873 | 1.001801 | 0.963078 | 1.022469 | 1.201443 | 1.055301 | 0.962590 | 0.912575 |
| 32 | Bulgaria | BG | TOTAL | normalized | 1.024011 | 0.977541 | 0.998447 | 0.988264 | 0.984702 | 1.150005 | 1.357649 | 1.080035 | 0.919335 | 0.911709 |
| 33 | Romania | RO | TOTAL | normalized | 1.020584 | 0.981718 | 0.997698 | 1.005356 | 0.993947 | 1.151277 | 1.276275 | 1.026653 | 0.918109 | 0.837779 |
Y20-39¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Iceland | IS | Y20-39 | normalized | 0.794118 | 1.023529 | 1.182353 | 1.200000 | 0.988235 | 1.147059 | 1.129412 | 1.182353 | 1.323529 | 1.217647 |
| 1 | Netherlands | NL | Y20-39 | normalized | 0.995277 | 1.007084 | 0.997639 | 1.011806 | 1.027745 | 1.087957 | 1.122786 | 1.131051 | 1.154073 | 1.190902 |
| 2 | Austria | AT | Y20-39 | normalized | 1.048147 | 0.980678 | 0.971175 | 1.017738 | 1.014888 | 0.999683 | 1.100412 | 1.123218 | 1.074755 | 1.153627 |
| 3 | Malta | MT | Y20-39 | normalized | 0.989189 | 1.021622 | 0.989189 | 1.151351 | 0.810811 | 1.151351 | 0.810811 | 1.054054 | 0.924324 | 1.054054 |
| 4 | Montenegro | ME | Y20-39 | normalized | 0.941019 | 0.924933 | 1.134048 | 0.844504 | 1.005362 | 0.997319 | 1.174263 | 1.013405 | 0.997319 | 1.028150 |
| 5 | Luxembourg | LU | Y20-39 | normalized | 0.994737 | 0.884211 | 1.121053 | 1.121053 | 1.168421 | 1.073684 | 0.978947 | 1.105263 | 0.978947 | 1.026316 |
| 6 | Norway | NO | Y20-39 | normalized | 1.017909 | 1.020813 | 0.961278 | 1.020813 | 1.019361 | 1.010649 | 0.920620 | 0.962730 | 1.122459 | 1.010649 |
| 7 | France | FR | Y20-39 | normalized | 1.035161 | 0.988817 | 0.976022 | 0.991828 | 0.991183 | 0.978710 | 0.997527 | 1.031290 | 1.004086 | 0.983548 |
| 8 | Portugal | PT | Y20-39 | normalized | 1.053143 | 0.942003 | 1.004854 | 0.924374 | 0.954267 | 0.997956 | 0.948901 | 0.960399 | 0.958099 | 0.961165 |
| 9 | Denmark | DK | Y20-39 | normalized | 0.959227 | 1.071888 | 0.968884 | 0.981760 | 0.976931 | 1.062232 | 0.939914 | 1.015558 | 0.984979 | 0.951180 |
| 10 | Finland | FI | Y20-39 | normalized | 0.976999 | 1.001095 | 1.021906 | 0.975904 | 1.085433 | 1.059146 | 0.949617 | 0.986857 | 1.047097 | 0.950712 |
| 11 | Belgium | BE | Y20-39 | normalized | 1.014289 | 1.005232 | 0.980479 | 0.951499 | 0.909237 | 0.927953 | 0.940028 | 0.906219 | 0.906219 | 0.913463 |
| 12 | Switzerland | CH | Y20-39 | normalized | 1.009773 | 0.981626 | 1.008600 | 0.948788 | 0.921814 | 0.966380 | 0.983972 | 1.002737 | 0.971071 | 0.907740 |
| 13 | Spain | ES | Y20-39 | normalized | 1.036045 | 0.980090 | 0.983865 | 0.958774 | 0.908371 | 0.995189 | 0.974761 | 0.960995 | 0.959218 | 0.906373 |
| 14 | Armenia | AM | Y20-39 | normalized | 1.066728 | 1.026965 | 0.906307 | 0.957038 | 0.822669 | 2.520110 | 1.542505 | 1.026965 | 0.874771 | 0.892710 |
| 15 | Slovakia | SK | Y20-39 | normalized | 1.059393 | 0.984413 | 0.956195 | 0.965869 | 0.981188 | 0.944907 | 1.099704 | 0.969901 | 0.902177 | 0.891696 |
| 16 | Hungary | HU | Y20-39 | normalized | 1.072598 | 0.951419 | 0.975983 | 0.948144 | 0.885371 | 0.967795 | 1.144651 | 0.925218 | 0.896834 | 0.887009 |
| 17 | Czechia | CZ | Y20-39 | normalized | 1.060503 | 0.984560 | 0.954937 | 0.979713 | 0.936625 | 0.945781 | 0.997487 | 0.960323 | 0.938241 | 0.877917 |
| 18 | Poland | PL | Y20-39 | normalized | 1.021684 | 0.990499 | 0.987817 | 0.996060 | 0.985632 | 1.040158 | 1.098656 | 1.035390 | 0.911342 | 0.844402 |
| 19 | Italy | IT | Y20-39 | normalized | 1.063570 | 0.990165 | 0.946265 | 0.953729 | 0.892944 | 0.881569 | 0.924048 | 0.883346 | 0.865395 | 0.834647 |
| 20 | Cyprus | CY | Y20-39 | normalized | 1.161290 | 0.832258 | 1.006452 | 1.006452 | 1.209677 | 1.122581 | 1.335484 | 0.929032 | 1.248387 | 0.832258 |
| 21 | Serbia | RS | Y20-39 | normalized | 1.083099 | 0.952113 | 0.964789 | 0.888732 | 0.884507 | 0.990141 | 1.138732 | 0.957042 | 0.571831 | 0.795070 |
| 22 | Estonia | EE | Y20-39 | normalized | 1.039134 | 1.099084 | 0.861782 | 0.756869 | 0.709409 | 0.741882 | 0.809326 | 0.851790 | 0.824313 | 0.794338 |
| 23 | Slovenia | SI | Y20-39 | normalized | 1.060811 | 1.037162 | 0.902027 | 0.875000 | 0.945946 | 0.837838 | 0.820946 | 0.956081 | 0.810811 | 0.793919 |
| 24 | Sweden | SE | Y20-39 | normalized | 1.054507 | 0.955597 | 0.989896 | 1.005850 | 0.901356 | 0.914119 | 0.883807 | 0.861473 | 0.906142 | 0.767349 |
| 25 | Bulgaria | BG | Y20-39 | normalized | 1.060354 | 1.010520 | 0.929125 | 0.954042 | 0.934662 | 0.932447 | 1.182724 | 0.883167 | 0.801772 | 0.761351 |
| 26 | Croatia | HR | Y20-39 | normalized | 1.107878 | 0.945309 | 0.946814 | 0.925740 | 0.939288 | 0.918214 | 0.955845 | 0.886603 | 0.812845 | 0.757150 |
| 27 | Greece | EL | Y20-39 | normalized | 1.070435 | 1.006182 | 0.923383 | 0.937955 | 0.892250 | 0.863767 | 0.948554 | 0.881652 | 0.835284 | 0.716715 |
| 28 | Romania | RO | Y20-39 | normalized | 1.072698 | 0.969709 | 0.957593 | 0.903877 | 0.897819 | 0.926898 | 0.991922 | 0.782512 | 0.751010 | 0.688813 |
| 29 | Lithuania | LT | Y20-39 | normalized | 1.135041 | 0.986612 | 0.878347 | 0.880093 | 0.798021 | 0.789290 | 0.850407 | 0.729045 | 0.709837 | 0.679278 |
| 30 | Latvia | LV | Y20-39 | normalized | 1.052184 | 0.978155 | 0.969660 | 0.867718 | 0.774272 | 0.743932 | 0.902913 | 0.870146 | 0.794903 | 0.675971 |
| 31 | Liechtenstein | LI | Y20-39 | normalized | 0.600000 | 1.500000 | 0.900000 | 1.800000 | 0.900000 | 0.900000 | 0.900000 | 0.000000 | 1.500000 | 0.600000 |
| 32 | Germany | DE | Y20-39 | normalized | 1.031710 | 0.986478 | 0.981813 | 0.984855 | 0.974004 | 0.997836 | 0.997025 | 1.033738 | 0.992056 | 0.000000 |
Y40-59¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Liechtenstein | LI | Y40-59 | normalized | 1.094595 | 0.851351 | 1.054054 | 0.891892 | 0.770270 | 0.648649 | 0.972973 | 0.000000 | 1.094595 | 1.013514 |
| 1 | Iceland | IS | Y40-59 | normalized | 1.093407 | 0.912088 | 0.994505 | 1.005495 | 1.115385 | 1.104396 | 1.060440 | 1.082418 | 1.258242 | 1.010989 |
| 2 | Czechia | CZ | Y40-59 | normalized | 1.035208 | 0.988029 | 0.976763 | 0.976059 | 0.972840 | 1.077859 | 1.230560 | 1.035711 | 1.001811 | 0.970526 |
| 3 | Norway | NO | Y40-59 | normalized | 1.036278 | 1.006382 | 0.957340 | 0.962042 | 0.921061 | 0.942224 | 0.926772 | 0.994290 | 0.956332 | 0.936177 |
| 4 | Estonia | EE | Y40-59 | normalized | 1.048814 | 1.014427 | 0.936759 | 0.974704 | 0.935573 | 0.992490 | 1.022134 | 0.972332 | 0.937352 | 0.931423 |
| 5 | Spain | ES | Y40-59 | normalized | 1.026911 | 0.987325 | 0.985764 | 0.983054 | 0.966589 | 1.053125 | 1.010711 | 0.996308 | 0.956045 | 0.929124 |
| 6 | Greece | EL | Y40-59 | normalized | 1.021378 | 0.994886 | 0.983737 | 0.964641 | 0.969056 | 1.012216 | 1.144345 | 1.012326 | 0.951505 | 0.909780 |
| 7 | Luxembourg | LU | Y40-59 | normalized | 0.984219 | 1.026578 | 0.989203 | 1.034053 | 0.996678 | 1.044020 | 1.041528 | 0.959302 | 0.941860 | 0.904485 |
| 8 | Cyprus | CY | Y40-59 | normalized | 1.015748 | 0.981389 | 1.002863 | 0.914817 | 0.970651 | 1.157480 | 1.262706 | 1.039370 | 1.088762 | 0.901933 |
| 9 | Netherlands | NL | Y40-59 | normalized | 1.026651 | 1.007007 | 0.966341 | 0.952298 | 0.920678 | 0.955055 | 0.989259 | 0.958329 | 0.903532 | 0.887899 |
| 10 | Austria | AT | Y40-59 | normalized | 1.038582 | 0.988316 | 0.973102 | 1.000099 | 0.942972 | 0.985184 | 0.995774 | 0.954905 | 0.905534 | 0.887784 |
| 11 | Bulgaria | BG | Y40-59 | normalized | 1.019183 | 0.995337 | 0.985480 | 0.973716 | 0.942875 | 1.098431 | 1.304859 | 0.983334 | 0.875550 | 0.886678 |
| 12 | Portugal | PT | Y40-59 | normalized | 1.004711 | 0.997906 | 0.997383 | 0.979483 | 0.943473 | 1.018005 | 0.987753 | 0.956768 | 0.915001 | 0.885690 |
| 13 | Italy | IT | Y40-59 | normalized | 1.036317 | 0.981260 | 0.982423 | 0.974773 | 0.953359 | 1.042061 | 1.032480 | 0.973140 | 0.925236 | 0.874882 |
| 14 | France | FR | Y40-59 | normalized | 1.042235 | 0.987464 | 0.970301 | 0.962393 | 0.928553 | 0.963311 | 0.949071 | 0.938990 | 0.893382 | 0.873726 |
| 15 | Montenegro | ME | Y40-59 | normalized | 1.082461 | 0.986911 | 0.930628 | 1.014398 | 1.048429 | 1.018325 | 1.267016 | 0.920157 | 0.829843 | 0.856457 |
| 16 | Malta | MT | Y40-59 | normalized | 0.974359 | 1.043956 | 0.981685 | 1.025641 | 0.941392 | 1.106227 | 1.084249 | 1.080586 | 0.970696 | 0.846154 |
| 17 | Germany | DE | Y40-59 | normalized | 1.033932 | 0.998634 | 0.967435 | 0.976447 | 0.935175 | 0.955307 | 0.972568 | 0.933598 | 0.885293 | 0.838836 |
| 18 | Slovakia | SK | Y40-59 | normalized | 1.066639 | 0.965570 | 0.967791 | 0.976260 | 0.922810 | 0.972928 | 1.208802 | 0.965848 | 0.877690 | 0.838817 |
| 19 | Serbia | RS | Y40-59 | normalized | 1.058988 | 0.973158 | 0.967854 | 0.926868 | 0.922528 | 1.101517 | 1.196509 | 0.890510 | 0.569275 | 0.835059 |
| 20 | Belgium | BE | Y40-59 | normalized | 1.061509 | 0.987417 | 0.951074 | 0.937338 | 0.899697 | 0.965460 | 0.920681 | 0.901103 | 0.854269 | 0.834151 |
| 21 | Finland | FI | Y40-59 | normalized | 1.042336 | 1.002190 | 0.955474 | 0.953041 | 0.889538 | 0.943552 | 0.910219 | 0.900973 | 0.875912 | 0.827494 |
| 22 | Hungary | HU | Y40-59 | normalized | 1.072314 | 0.976552 | 0.951134 | 0.931626 | 0.895024 | 0.950181 | 1.122450 | 0.904620 | 0.824236 | 0.820995 |
| 23 | Switzerland | CH | Y40-59 | normalized | 1.035309 | 0.975200 | 0.989491 | 0.974989 | 0.914880 | 0.922657 | 0.942833 | 0.934636 | 0.876629 | 0.817781 |
| 24 | Latvia | LV | Y40-59 | normalized | 1.039009 | 0.992923 | 0.968068 | 0.981013 | 0.898680 | 0.906447 | 1.115647 | 0.944766 | 0.859066 | 0.813498 |
| 25 | Romania | RO | Y40-59 | normalized | 1.028697 | 0.997176 | 0.974128 | 0.976735 | 0.971106 | 1.053908 | 1.180850 | 0.948532 | 0.886615 | 0.806273 |
| 26 | Sweden | SE | Y40-59 | normalized | 1.059663 | 0.985254 | 0.955083 | 0.929397 | 0.890459 | 0.941628 | 0.913699 | 0.876597 | 0.868443 | 0.800761 |
| 27 | Poland | PL | Y40-59 | normalized | 1.051089 | 0.987253 | 0.961658 | 0.940221 | 0.895069 | 0.957177 | 1.046741 | 0.894082 | 0.811068 | 0.792992 |
| 28 | Croatia | HR | Y40-59 | normalized | 1.084327 | 0.956285 | 0.959388 | 0.958147 | 0.905399 | 0.924843 | 1.029511 | 0.866510 | 0.841274 | 0.782941 |
| 29 | Slovenia | SI | Y40-59 | normalized | 1.060018 | 0.975601 | 0.964381 | 0.913624 | 0.875156 | 0.959038 | 0.970793 | 0.875690 | 0.855387 | 0.778451 |
| 30 | Denmark | DK | Y40-59 | normalized | 1.042183 | 1.006376 | 0.951442 | 0.957002 | 0.890503 | 0.878494 | 0.875380 | 0.862481 | 0.824672 | 0.777522 |
| 31 | Lithuania | LT | Y40-59 | normalized | 1.063919 | 1.029615 | 0.906466 | 0.860671 | 0.836659 | 0.985707 | 1.007490 | 0.845235 | 0.789320 | 0.775256 |
| 32 | Armenia | AM | Y40-59 | normalized | 1.065157 | 1.011936 | 0.922907 | 0.891024 | 0.845405 | 1.067119 | 1.026161 | 0.775262 | 0.707325 | 0.713129 |
Y60-79¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Iceland | IS | Y60-79 | normalized | 0.936424 | 1.078394 | 0.985182 | 1.013862 | 1.006692 | 1.104207 | 1.135755 | 1.346558 | 1.197419 | 1.300669 |
| 1 | Malta | MT | Y60-79 | normalized | 1.027934 | 0.945669 | 1.026397 | 1.068683 | 1.065607 | 1.194003 | 1.255510 | 1.257048 | 1.183239 | 1.253203 |
| 2 | France | FR | Y60-79 | normalized | 1.001712 | 0.990021 | 1.008267 | 1.022124 | 1.029084 | 1.147847 | 1.175806 | 1.192257 | 1.156656 | 1.161545 |
| 3 | Netherlands | NL | Y60-79 | normalized | 1.006386 | 0.996193 | 0.997421 | 1.028461 | 1.022510 | 1.152640 | 1.182317 | 1.161681 | 1.160779 | 1.160364 |
| 4 | Norway | NO | Y60-79 | normalized | 0.996253 | 0.995265 | 1.008483 | 1.026410 | 1.037576 | 1.058010 | 1.088243 | 1.184716 | 1.142101 | 1.152432 |
| 5 | Liechtenstein | LI | Y60-79 | normalized | 0.822742 | 1.153846 | 1.023411 | 1.023411 | 1.013378 | 1.193980 | 0.983278 | 0.000000 | 0.872910 | 1.143813 |
| 6 | Poland | PL | Y60-79 | normalized | 1.006538 | 0.979267 | 1.014195 | 1.052696 | 1.062426 | 1.268973 | 1.415998 | 1.196846 | 1.120739 | 1.118838 |
| 7 | Spain | ES | Y60-79 | normalized | 1.030616 | 0.982768 | 0.986616 | 1.004716 | 1.007285 | 1.209219 | 1.147454 | 1.157316 | 1.126992 | 1.117345 |
| 8 | Cyprus | CY | Y60-79 | normalized | 1.023144 | 0.956687 | 1.020169 | 1.042486 | 1.065796 | 1.142172 | 1.285998 | 1.265664 | 1.143660 | 1.115391 |
| 9 | Finland | FI | Y60-79 | normalized | 0.993128 | 1.000244 | 1.006628 | 1.008721 | 1.004221 | 1.043101 | 1.073660 | 1.138337 | 1.151367 | 1.104900 |
| 10 | Belgium | BE | Y60-79 | normalized | 1.021779 | 0.986703 | 0.991518 | 0.997160 | 0.983882 | 1.137149 | 1.080190 | 1.089907 | 1.057168 | 1.066001 |
| 11 | Slovakia | SK | Y60-79 | normalized | 1.008638 | 0.979238 | 1.012124 | 1.033819 | 1.037392 | 1.191537 | 1.516013 | 1.171823 | 1.086851 | 1.062229 |
| 12 | Portugal | PT | Y60-79 | normalized | 1.015604 | 1.007187 | 0.977209 | 1.000874 | 0.986145 | 1.099310 | 1.112425 | 1.088357 | 1.051461 | 1.035291 |
| 13 | Luxembourg | LU | Y60-79 | normalized | 0.977408 | 0.925327 | 1.097265 | 1.021641 | 0.999524 | 1.081570 | 1.071581 | 1.036623 | 1.049465 | 1.034483 |
| 14 | Austria | AT | Y60-79 | normalized | 1.010726 | 0.987562 | 1.001712 | 1.026982 | 1.016490 | 1.110993 | 1.098728 | 1.089344 | 1.048483 | 1.032006 |
| 15 | Denmark | DK | Y60-79 | normalized | 1.005923 | 0.997159 | 0.996918 | 1.035054 | 1.032309 | 1.054844 | 1.079883 | 1.118500 | 1.080508 | 1.027061 |
| 16 | Slovenia | SI | Y60-79 | normalized | 1.020434 | 0.964492 | 1.015074 | 1.028681 | 1.034592 | 1.145652 | 1.174792 | 1.093833 | 1.049024 | 1.023871 |
| 17 | Greece | EL | Y60-79 | normalized | 1.027313 | 0.977934 | 0.994753 | 0.953931 | 0.961493 | 1.043055 | 1.169151 | 1.103654 | 1.039826 | 1.023034 |
| 18 | Armenia | AM | Y60-79 | normalized | 1.022296 | 1.003221 | 0.974483 | 0.898186 | 0.884368 | 1.199729 | 1.282214 | 0.946677 | 0.933198 | 1.017760 |
| 19 | Switzerland | CH | Y60-79 | normalized | 1.035228 | 0.972487 | 0.992286 | 1.004731 | 1.023708 | 1.102237 | 1.093854 | 1.090666 | 1.048650 | 1.017639 |
| 20 | Czechia | CZ | Y60-79 | normalized | 1.010894 | 0.976813 | 1.012294 | 1.040273 | 1.037745 | 1.207502 | 1.362255 | 1.101247 | 1.029428 | 0.999694 |
| 21 | Hungary | HU | Y60-79 | normalized | 1.007109 | 0.974702 | 1.018189 | 1.019195 | 1.012427 | 1.137502 | 1.266804 | 1.079099 | 1.020917 | 0.993624 |
| 22 | Montenegro | ME | Y60-79 | normalized | 1.001448 | 1.017815 | 0.980737 | 0.977063 | 0.965037 | 1.124374 | 1.418662 | 1.047545 | 0.980069 | 0.985024 |
| 23 | Estonia | EE | Y60-79 | normalized | 1.024266 | 0.984170 | 0.991564 | 0.994029 | 0.960999 | 0.979240 | 1.138475 | 1.014078 | 0.958863 | 0.981047 |
| 24 | Germany | DE | Y60-79 | normalized | 1.027855 | 0.985397 | 0.986748 | 0.995439 | 0.963581 | 0.989393 | 1.005570 | 1.019991 | 0.998744 | 0.972759 |
| 25 | Serbia | RS | Y60-79 | normalized | 1.030788 | 0.976346 | 0.992867 | 0.975233 | 0.982402 | 1.186175 | 1.375226 | 1.026807 | 0.664629 | 0.971338 |
| 26 | Bulgaria | BG | Y60-79 | normalized | 1.017113 | 0.983431 | 0.999455 | 0.996418 | 1.000251 | 1.207767 | 1.489684 | 1.136026 | 0.972162 | 0.968141 |
| 27 | Lithuania | LT | Y60-79 | normalized | 1.044912 | 1.002053 | 0.953035 | 0.948736 | 0.923008 | 1.092519 | 1.164314 | 1.027974 | 0.916271 | 0.939625 |
| 28 | Sweden | SE | Y60-79 | normalized | 1.016499 | 0.992130 | 0.991371 | 1.010873 | 0.974664 | 1.083152 | 1.029478 | 1.020710 | 1.003072 | 0.937179 |
| 29 | Croatia | HR | Y60-79 | normalized | 1.045235 | 0.965003 | 0.989761 | 0.961170 | 0.938629 | 1.070594 | 1.163621 | 1.023295 | 0.951701 | 0.930869 |
| 30 | Italy | IT | Y60-79 | normalized | 1.036313 | 0.968224 | 0.995463 | 0.962589 | 0.946423 | 1.117576 | 1.063062 | 1.018156 | 0.959284 | 0.929829 |
| 31 | Latvia | LV | Y60-79 | normalized | 1.014798 | 0.991500 | 0.993702 | 0.975826 | 0.928212 | 0.982858 | 1.155182 | 0.985145 | 0.917876 | 0.855860 |
| 32 | Romania | RO | Y60-79 | normalized | 1.020654 | 0.984388 | 0.994957 | 0.998664 | 0.985260 | 1.171096 | 1.331617 | 1.027356 | 0.931207 | 0.848400 |
Y_GE80¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Malta | MT | Y_GE80 | normalized | 1.016626 | 0.944891 | 1.038483 | 1.063703 | 1.090043 | 1.230712 | 1.195404 | 1.230151 | 1.197646 | 1.202690 |
| 1 | Germany | DE | Y_GE80 | normalized | 1.006407 | 0.971699 | 1.021894 | 1.060079 | 1.056850 | 1.164951 | 1.186592 | 1.271628 | 1.214897 | 1.187499 |
| 2 | Netherlands | NL | Y_GE80 | normalized | 0.999626 | 0.988800 | 1.011575 | 1.032805 | 1.023210 | 1.173788 | 1.137483 | 1.141598 | 1.145123 | 1.168635 |
| 3 | Cyprus | CY | Y_GE80 | normalized | 1.031191 | 0.919405 | 1.049403 | 0.978438 | 1.083316 | 1.147059 | 1.209546 | 1.261356 | 1.156165 | 1.168411 |
| 4 | Slovenia | SI | Y_GE80 | normalized | 0.984707 | 0.980338 | 1.034954 | 1.033910 | 1.046068 | 1.325133 | 1.177432 | 1.185125 | 1.131839 | 1.147036 |
| 5 | Luxembourg | LU | Y_GE80 | normalized | 1.002755 | 0.983930 | 1.013315 | 1.076217 | 1.089532 | 1.202020 | 1.134068 | 1.145546 | 1.129936 | 1.146924 |
| 6 | Denmark | DK | Y_GE80 | normalized | 1.004042 | 0.983036 | 1.012922 | 1.055755 | 1.023407 | 1.068441 | 1.110229 | 1.164853 | 1.148548 | 1.144705 |
| 7 | Liechtenstein | LI | Y_GE80 | normalized | 1.119171 | 0.979275 | 0.901554 | 1.103627 | 1.064767 | 1.398964 | 1.088083 | 0.000000 | 1.088083 | 1.142487 |
| 8 | Portugal | PT | Y_GE80 | normalized | 1.000130 | 0.997815 | 1.002055 | 1.045915 | 1.040470 | 1.185789 | 1.164868 | 1.178841 | 1.109225 | 1.116408 |
| 9 | Austria | AT | Y_GE80 | normalized | 1.029105 | 0.961256 | 1.009639 | 1.007036 | 1.009617 | 1.158656 | 1.126897 | 1.182519 | 1.128778 | 1.114539 |
| 10 | Estonia | EE | Y_GE80 | normalized | 0.972399 | 0.989295 | 1.038306 | 1.062462 | 1.054364 | 1.126135 | 1.306539 | 1.252921 | 1.134512 | 1.112311 |
| 11 | Finland | FI | Y_GE80 | normalized | 0.993130 | 1.006171 | 1.000700 | 1.032529 | 1.018627 | 1.073476 | 1.107473 | 1.210323 | 1.179802 | 1.101244 |
| 12 | Iceland | IS | Y_GE80 | normalized | 1.006457 | 1.008006 | 0.985537 | 0.985537 | 1.001033 | 0.995610 | 0.977014 | 1.138171 | 1.087035 | 1.092459 |
| 13 | Switzerland | CH | Y_GE80 | normalized | 1.028260 | 0.966928 | 1.004811 | 1.005978 | 1.020995 | 1.183948 | 1.057906 | 1.143878 | 1.105314 | 1.078536 |
| 14 | Greece | EL | Y_GE80 | normalized | 1.002392 | 0.960390 | 1.037218 | 1.004366 | 1.063222 | 1.132983 | 1.196455 | 1.201615 | 1.070628 | 1.051214 |
| 15 | Norway | NO | Y_GE80 | normalized | 1.025010 | 0.983224 | 0.991766 | 0.984739 | 0.974092 | 0.989915 | 1.008556 | 1.099452 | 1.035530 | 1.048029 |
| 16 | France | FR | Y_GE80 | normalized | 1.004114 | 0.982417 | 1.013469 | 1.018071 | 1.018234 | 1.142014 | 1.078183 | 1.110447 | 1.036079 | 1.038563 |
| 17 | Italy | IT | Y_GE80 | normalized | 1.023231 | 0.953990 | 1.022779 | 0.994764 | 1.016269 | 1.206237 | 1.108397 | 1.148491 | 1.051627 | 1.027454 |
| 18 | Spain | ES | Y_GE80 | normalized | 1.019018 | 0.963549 | 1.017433 | 1.023420 | 0.990805 | 1.194317 | 1.047491 | 1.093311 | 1.013904 | 1.026137 |
| 19 | Belgium | BE | Y_GE80 | normalized | 1.020706 | 0.974733 | 1.004561 | 1.021500 | 1.005688 | 1.228524 | 1.004942 | 1.069950 | 1.018007 | 1.020770 |
| 20 | Czechia | CZ | Y_GE80 | normalized | 1.034472 | 0.960345 | 1.005183 | 1.009424 | 1.002595 | 1.208808 | 1.181531 | 1.085303 | 1.012929 | 1.020256 |
| 21 | Latvia | LV | Y_GE80 | normalized | 0.992681 | 0.991351 | 1.015968 | 1.045492 | 1.032685 | 1.120758 | 1.303560 | 1.175815 | 1.052146 | 1.019128 |
| 22 | Sweden | SE | Y_GE80 | normalized | 1.016228 | 0.982756 | 1.001015 | 0.992911 | 0.954879 | 1.093806 | 0.972565 | 1.023096 | 1.023041 | 1.008917 |
| 23 | Poland | PL | Y_GE80 | normalized | 1.004048 | 0.964785 | 1.031168 | 1.052632 | 1.037325 | 1.278953 | 1.301091 | 1.139304 | 1.015423 | 1.000104 |
| 24 | Croatia | HR | Y_GE80 | normalized | 1.017100 | 0.963122 | 1.019778 | 1.017294 | 1.009650 | 1.141163 | 1.219822 | 1.148148 | 0.993778 | 0.999289 |
| 25 | Montenegro | ME | Y_GE80 | normalized | 0.975237 | 0.970495 | 1.054268 | 1.042018 | 1.073630 | 1.200474 | 1.470364 | 1.183483 | 1.018704 | 0.997432 |
| 26 | Hungary | HU | Y_GE80 | normalized | 1.032011 | 0.957518 | 1.010471 | 1.005189 | 0.998880 | 1.104543 | 1.126661 | 1.047260 | 0.983313 | 0.987980 |
| 27 | Slovakia | SK | Y_GE80 | normalized | 1.021014 | 0.968713 | 1.010273 | 1.002392 | 0.967963 | 1.119799 | 1.275623 | 1.101506 | 0.975140 | 0.984474 |
| 28 | Lithuania | LT | Y_GE80 | normalized | 1.006995 | 0.974970 | 1.018035 | 1.006394 | 0.972948 | 1.118155 | 1.218275 | 1.117499 | 0.980435 | 0.959067 |
| 29 | Serbia | RS | Y_GE80 | normalized | 1.008114 | 0.972719 | 1.019168 | 1.001801 | 0.992752 | 1.143336 | 1.297839 | 1.033937 | 0.630527 | 0.904344 |
| 30 | Armenia | AM | Y_GE80 | normalized | 0.966802 | 1.005230 | 1.027967 | 0.962574 | 1.006170 | 1.322612 | 1.314908 | 1.052490 | 0.875102 | 0.898458 |
| 31 | Bulgaria | BG | Y_GE80 | normalized | 1.030293 | 0.965615 | 1.004092 | 0.985744 | 0.983319 | 1.119563 | 1.251279 | 1.061838 | 0.884221 | 0.870080 |
| 32 | Romania | RO | Y_GE80 | normalized | 1.014486 | 0.974899 | 1.010615 | 1.029126 | 1.018134 | 1.180326 | 1.273844 | 1.068713 | 0.927839 | 0.849322 |
Y_LT20¶
| index | name | abbr | filter | value_type | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Luxembourg | LU | Y_LT20 | normalized | 1.000000 | 0.944444 | 1.055556 | 1.055556 | 1.111111 | 1.277778 | 1.055556 | 1.111111 | 1.138889 | 2.638889 |
| 1 | Cyprus | CY | Y_LT20 | normalized | 0.992308 | 1.107692 | 0.900000 | 1.153846 | 0.969231 | 0.900000 | 1.107692 | 1.361538 | 1.476923 | 1.384615 |
| 2 | Denmark | DK | Y_LT20 | normalized | 0.999054 | 0.947966 | 1.052980 | 1.052980 | 0.911069 | 0.922422 | 0.959319 | 0.896878 | 0.882687 | 1.092715 |
| 3 | Iceland | IS | Y_LT20 | normalized | 1.173913 | 0.782609 | 1.043478 | 0.869565 | 0.434783 | 1.043478 | 1.217391 | 0.782609 | 1.000000 | 1.043478 |
| 4 | Norway | NO | Y_LT20 | normalized | 0.979592 | 0.993998 | 1.026411 | 0.911164 | 0.979592 | 0.929172 | 0.900360 | 0.893157 | 1.044418 | 0.986795 |
| 5 | Netherlands | NL | Y_LT20 | normalized | 0.966754 | 0.980579 | 1.052666 | 0.978604 | 0.999342 | 1.036866 | 0.950955 | 1.004279 | 1.012179 | 0.978786 |
| 6 | Austria | AT | Y_LT20 | normalized | 0.995976 | 1.016097 | 0.987928 | 0.901408 | 0.993964 | 0.993964 | 0.891348 | 0.891348 | 0.963783 | 0.975855 |
| 7 | Finland | FI | Y_LT20 | normalized | 0.941687 | 0.975186 | 1.083127 | 1.034739 | 1.016129 | 0.971464 | 0.993797 | 0.964020 | 1.057072 | 0.956576 |
| 8 | Spain | ES | Y_LT20 | normalized | 1.025691 | 0.994183 | 0.980126 | 0.950557 | 0.915657 | 0.854581 | 0.881726 | 0.951527 | 0.912748 | 0.949103 |
| 9 | France | FR | Y_LT20 | normalized | 1.039097 | 0.983797 | 0.977106 | 0.963527 | 0.962149 | 0.899764 | 0.909210 | 0.981829 | 0.935778 | 0.944831 |
| 10 | Portugal | PT | Y_LT20 | normalized | 1.017308 | 1.046154 | 0.936538 | 1.069231 | 0.965385 | 0.890385 | 0.871154 | 0.963462 | 0.965385 | 0.900000 |
| 11 | Serbia | RS | Y_LT20 | normalized | 1.037875 | 0.993782 | 0.968344 | 0.954777 | 0.937818 | 0.929339 | 0.900509 | 0.744488 | 0.581685 | 0.883550 |
| 12 | Czechia | CZ | Y_LT20 | normalized | 0.976979 | 1.022459 | 1.000561 | 1.010668 | 1.030882 | 0.882650 | 0.892757 | 0.941606 | 0.840539 | 0.869175 |
| 13 | Slovakia | SK | Y_LT20 | normalized | 1.064392 | 0.981326 | 0.954282 | 1.004507 | 0.994849 | 1.025757 | 0.925306 | 0.952350 | 0.915647 | 0.863490 |
| 14 | Hungary | HU | Y_LT20 | normalized | 1.068474 | 0.987161 | 0.944365 | 0.821683 | 0.918688 | 0.828816 | 0.850214 | 0.861626 | 0.766049 | 0.843081 |
| 15 | Estonia | EE | Y_LT20 | normalized | 1.104089 | 0.992565 | 0.903346 | 0.881041 | 0.791822 | 0.691450 | 0.925651 | 0.847584 | 0.914498 | 0.836431 |
| 16 | Montenegro | ME | Y_LT20 | normalized | 0.919708 | 1.138686 | 0.941606 | 0.810219 | 1.029197 | 0.963504 | 0.722628 | 1.467153 | 0.963504 | 0.830292 |
| 17 | Sweden | SE | Y_LT20 | normalized | 1.033414 | 0.989672 | 0.976914 | 0.960510 | 0.874848 | 1.018834 | 0.860267 | 0.936817 | 0.889429 | 0.816525 |
| 18 | Slovenia | SI | Y_LT20 | normalized | 1.003521 | 1.119718 | 0.876761 | 0.950704 | 0.940141 | 0.802817 | 0.908451 | 0.887324 | 0.876761 | 0.813380 |
| 19 | Greece | EL | Y_LT20 | normalized | 0.995420 | 1.070229 | 0.934351 | 0.874809 | 0.882443 | 0.786260 | 0.824427 | 0.758779 | 0.778626 | 0.801527 |
| 20 | Switzerland | CH | Y_LT20 | normalized | 1.097222 | 0.932540 | 0.970238 | 1.000000 | 0.898810 | 0.974206 | 0.928571 | 1.013889 | 0.894841 | 0.791667 |
| 21 | Poland | PL | Y_LT20 | normalized | 1.029240 | 0.976952 | 0.993808 | 0.976264 | 0.911249 | 0.826281 | 0.898865 | 0.883385 | 0.810802 | 0.740282 |
| 22 | Italy | IT | Y_LT20 | normalized | 1.055676 | 0.974331 | 0.969993 | 0.977946 | 0.831164 | 0.781996 | 0.783080 | 0.734635 | 0.760304 | 0.732104 |
| 23 | Belgium | BE | Y_LT20 | normalized | 1.011116 | 0.944420 | 1.044464 | 1.008448 | 0.975100 | 0.896398 | 0.897732 | 0.875056 | 0.716318 | 0.705647 |
| 24 | Bulgaria | BG | Y_LT20 | normalized | 1.065617 | 0.965879 | 0.968504 | 0.952756 | 0.904199 | 0.759843 | 0.876640 | 0.748031 | 0.758530 | 0.691601 |
| 25 | Croatia | HR | Y_LT20 | normalized | 1.070718 | 0.967956 | 0.961326 | 0.951381 | 0.891713 | 0.911602 | 0.855249 | 0.848619 | 0.828729 | 0.672928 |
| 26 | Armenia | AM | Y_LT20 | normalized | 1.044905 | 1.079447 | 0.875648 | 0.782383 | 0.692573 | 2.015544 | 1.417962 | 0.958549 | 0.796200 | 0.624928 |
| 27 | Malta | MT | Y_LT20 | normalized | 1.008000 | 1.056000 | 0.936000 | 0.792000 | 1.104000 | 0.720000 | 0.672000 | 0.720000 | 0.576000 | 0.600000 |
| 28 | Romania | RO | Y_LT20 | normalized | 1.065895 | 0.971632 | 0.962473 | 0.873553 | 0.846076 | 0.781580 | 0.767078 | 0.730823 | 0.676250 | 0.593436 |
| 29 | Latvia | LV | Y_LT20 | normalized | 1.005650 | 1.000000 | 0.994350 | 0.847458 | 0.847458 | 0.819209 | 0.598870 | 0.655367 | 0.632768 | 0.519774 |
| 30 | Lithuania | LT | Y_LT20 | normalized | 1.185786 | 1.002494 | 0.811721 | 0.837905 | 0.759352 | 0.684539 | 0.658354 | 0.549875 | 0.620948 | 0.486284 |
| 31 | Liechtenstein | LI | Y_LT20 | normalized | 1.285714 | 1.285714 | 0.428571 | 0.428571 | 0.000000 | 3.000000 | 0.857143 | 0.000000 | 2.142857 | 0.428571 |
| 32 | Germany | DE | Y_LT20 | normalized | 0.983753 | 1.040361 | 0.975885 | 0.992277 | 0.965176 | 0.912502 | 0.927364 | 0.960367 | 0.951188 | 0.000000 |
Post HeatMaps Notes¶
More Deaths on the larger age groups
- European countries have strongly recommended COVID-19 vaccination for older adults due to their increased risk of severe illness.
- The European Centre for Disease Prevention and Control (ECDC) has emphasized the importance of vaccinating high-risk groups, including the elderly.
Iceland and Netherlands Y20-39 agegroup
- dispite being young this agegroup had a higher than usual death rate in 2022,2023,2024.
- Possible reasons:
- colder-climate?
- but Finland, and Sweden are futher north than netherlands
- vaccine hesitancy? - we will see the vaccination rate of all countries later in this document
- Young people feeling compelled to be vaccinated due to needing a
Covid/Corona Pass (or other name)- but many countries had these passes (see table below)
- How were they enforced (in each country) ? subjective
- Do contries have a nightlife or culture that compelled people?
- but many countries had these passes (see table below)
- colder-climate?
- Possible reasons:
- dispite being young this agegroup had a higher than usual death rate in 2022,2023,2024.
| Country | Name | Used For | Active Period | Notes |
|---|---|---|---|---|
| 🇫🇷 France | Pass Sanitaire / Passe Vaccinal | Restaurants, cafés, cinemas, museums, transport, hospitals | July 2021 – March 2022 | |
| 🇮🇹 Italy | Green Pass / Super Green Pass | Workplaces, restaurants, domestic travel, gyms, theaters, stadiums | June 2021 – April 2022 | |
| 🇦🇹 Austria | 3G/2G Rule | Restaurants, hotels, salons, gyms, public transport | Mid-2021 – March 2022 | |
| 🇩🇪 Germany | 3G/2G Rule | Restaurants, events, shops, workplaces, inter-regional travel | 2021 – March 2022 | Varied by region |
| 🇨🇭 Switzerland | COVID Certificate | Indoor dining, large events, cinemas, nightlife | Sept 2021 – Feb 2022 | |
| 🇳🇱 Netherlands | CoronaToegangsbewijs | Restaurants, bars, events, festivals, sports | Sept 2021 – March 2022 | Tests always accepted as alternative |
| 🇧🇪 Belgium | Covid Safe Ticket | Hospitality venues, events, cultural spaces, gyms | July 2021 – March 2022 | |
| 🇸🇮 Slovenia | PCT Certificate | Public services, work, shops, cafes, public transport | 2021 – Feb 2022 | |
| 🇱🇹 Lithuania | Opportunity Pass | Supermarkets, shops, gyms, restaurants, some healthcare | Mid-2021 – Feb 2022 | One of the most strict implementations |
| 🇱🇻 Latvia | COVID-19 Certificate | Workplaces, shops, services, restaurants, events | 2021 – Feb 2022 | |
| 🇱🇺 Luxembourg | CovidCheck | Workplaces, bars, restaurants, events | 2021 – Feb 2022 | Optional per employer |
| 🇨🇿 Czech Republic | O-N-T System | Restaurants, hotels, cultural events | 2021 – Feb 2022 | |
| 🇵🇱 Poland | EU COVID Certificate | Recommended for travel, limited venue use | — | No widespread domestic mandate |
| 🇵🇹 Portugal | Digital COVID Certificate | Hotels, restaurants (high-risk), events, air travel | Mid-2021 – Early 2022 | |
| 🇪🇸 Spain | EU Digital COVID Certificate | Regional use, nightlife, restaurants, hospitals | — | Highly decentralized use |
| 🇮🇸 Iceland | COVID-19 Certificate | International travel, some large events | — | Never broadly required, all restrictions lifted Feb 2022 |
| 🇩🇰 Denmark | Coronapas | Restaurants, gyms, salons | April 2021 – Sept 2021 | First to implement and one of the first to end it |
| 🇸🇪 Sweden | COVID Certificate | Only large indoor events (>100 people) | — | Never used for shops or restaurants, mostly optional |
| 🇫🇮 Finland | COVID Passport | Events, hospitality | — | Regional use only, phased out early 2022 |
display(MD('### HeatMap (of deaths and vaccines)'))
temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]
temp = pd.pivot_table(
data = temp,
values = 'value',
index = ['abbr','name','filter','value_type'],
columns=['year'],
aggfunc='mean',
)
temp.columns.name = 'index'
temp = temp.reset_index()
temp = temp.set_index('name').reindex(sppd['name']).reset_index()
temp['average(2022-2024)'] = (temp[2022]+temp[2023]+ temp[2024])/3.0
temp = temp.sort_values(by='average(2022-2024)',ascending=False)
## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]
temp = pd.merge(temp,v2023, how='left',on='name')
years = [y for y in range(2015,2025,1)]
styled_df = temp.style.background_gradient(cmap=heatmapCM1, axis=1, subset=years) \
.background_gradient(cmap=heatmapCM2, axis=0, subset=['average(2022-2024)']) \
.background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
display(MD('* 2015-2024 are the deaths for those years'))
display(MD('* average(2022-2024) is how the table is sorted'))
display(MD('* value is the total_doses/pop. for 2023'))
display(styled_df)
display(MD('### Lets sort by the vaccines '))
temp = temp.sort_values(by='value',ascending=False)
styled_df = temp.style.background_gradient(cmap=heatmapCM1, axis=1, subset=years) \
.background_gradient(cmap=heatmapCM2, axis=0, subset=['average(2022-2024)']) \
.background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
display(styled_df)
dxv = temp.copy()
HeatMap (of deaths and vaccines)¶
- 2015-2024 are the deaths for those years
- average(2022-2024) is how the table is sorted
- value is the total_doses/pop. for 2023
| name | abbr | filter_x | value_type_x | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | average(2022-2024) | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Malta | MT | TOTAL | normalized | 1.016949 | 0.955701 | 1.027350 | 1.060863 | 1.064330 | 1.199538 | 1.196071 | 1.219183 | 1.161980 | 1.183648 | 1.188270 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 1 | Cyprus | CY | TOTAL | normalized | 1.029186 | 0.937156 | 1.033658 | 0.997362 | 1.069610 | 1.143922 | 1.241800 | 1.239908 | 1.150459 | 1.124312 | 1.171560 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 2 | Iceland | IS | TOTAL | normalized | 0.988146 | 1.020151 | 0.991702 | 1.000148 | 1.005927 | 1.042377 | 1.039265 | 1.195733 | 1.140169 | 1.153060 | 1.162987 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 3 | Netherlands | NL | TOTAL | normalized | 1.003373 | 0.993167 | 1.003460 | 1.024649 | 1.015107 | 1.147749 | 1.140424 | 1.133556 | 1.131250 | 1.143136 | 1.135981 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 4 | Finland | FI | TOTAL | normalized | 0.996378 | 1.003504 | 1.000118 | 1.016952 | 1.004682 | 1.051873 | 1.076956 | 1.155757 | 1.143393 | 1.078209 | 1.125786 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 5 | Germany | DE | TOTAL | normalized | 1.016481 | 0.979311 | 1.004209 | 1.029001 | 1.012227 | 1.082065 | 1.101127 | 1.149954 | 1.106922 | 1.079127 | 1.112001 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 6 | Austria | AT | TOTAL | normalized | 1.023795 | 0.972870 | 1.003336 | 1.012615 | 1.006371 | 1.125312 | 1.104853 | 1.130013 | 1.081778 | 1.067871 | 1.093221 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 7 | Denmark | DK | TOTAL | normalized | 1.007454 | 0.991352 | 1.001194 | 1.038393 | 1.014429 | 1.045972 | 1.075440 | 1.117541 | 1.090769 | 1.064901 | 1.091070 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 8 | Portugal | PT | TOTAL | normalized | 1.006119 | 1.000327 | 0.993554 | 1.024547 | 1.013474 | 1.140328 | 1.128946 | 1.127418 | 1.071651 | 1.067896 | 1.088988 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 9 | Luxembourg | LU | TOTAL | normalized | 0.992076 | 0.966098 | 1.041827 | 1.053835 | 1.050895 | 1.143779 | 1.100400 | 1.089127 | 1.081529 | 1.095744 | 1.088800 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 10 | Norway | NO | TOTAL | normalized | 1.016140 | 0.989495 | 0.994364 | 0.996615 | 0.991477 | 1.008311 | 1.026049 | 1.115550 | 1.065612 | 1.072463 | 1.084542 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 11 | Slovenia | SI | TOTAL | normalized | 1.005897 | 0.975647 | 1.018456 | 1.018057 | 1.023988 | 1.216257 | 1.150672 | 1.118378 | 1.070087 | 1.061216 | 1.083227 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 12 | France | FR | TOTAL | normalized | 1.007603 | 0.985137 | 1.007261 | 1.013398 | 1.019252 | 1.131754 | 1.099201 | 1.122302 | 1.061615 | 1.062469 | 1.082129 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 13 | Greece | EL | TOTAL | normalized | 1.012210 | 0.969465 | 1.018325 | 0.984534 | 1.022175 | 1.091374 | 1.179188 | 1.151479 | 1.047889 | 1.026689 | 1.075352 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 14 | Switzerland | CH | TOTAL | normalized | 1.031079 | 0.969066 | 0.999855 | 1.002628 | 1.012027 | 1.137130 | 1.058255 | 1.110659 | 1.069182 | 1.037839 | 1.072560 | nan | nan | nan | nan |
| 15 | Liechtenstein | LI | TOTAL | normalized | 0.997423 | 1.043814 | 0.958763 | 1.055412 | 1.005155 | 1.256443 | 1.032216 | 1.078608 | 1.020619 | 1.117268 | 1.072165 | vaccine | All | ratio (total_dose/pop.) | 1.815763 |
| 16 | Spain | ES | TOTAL | normalized | 1.023104 | 0.971158 | 1.005738 | 1.013880 | 0.992168 | 1.192895 | 1.070740 | 1.101128 | 1.039692 | 1.042102 | 1.060974 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 17 | Estonia | EE | TOTAL | normalized | 1.003717 | 0.992890 | 1.003393 | 1.016879 | 0.994057 | 1.041062 | 1.194009 | 1.115234 | 1.034320 | 1.030883 | 1.060146 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 18 | Poland | PL | TOTAL | normalized | 1.011934 | 0.974416 | 1.013649 | 1.035756 | 1.026469 | 1.222837 | 1.306178 | 1.125759 | 1.027167 | 1.015246 | 1.056057 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 19 | Slovakia | SK | TOTAL | normalized | 1.023092 | 0.973345 | 1.003563 | 1.011692 | 0.992593 | 1.126210 | 1.363524 | 1.109279 | 1.008254 | 0.995303 | 1.037612 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 20 | Czechia | CZ | TOTAL | normalized | 1.024453 | 0.970711 | 1.004837 | 1.019293 | 1.014188 | 1.190285 | 1.259588 | 1.084875 | 1.016894 | 1.003669 | 1.035146 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 21 | Italy | IT | TOTAL | normalized | 1.028396 | 0.960289 | 1.011315 | 0.983592 | 0.989873 | 1.165225 | 1.087305 | 1.095037 | 1.013642 | 0.986208 | 1.031629 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 22 | Belgium | BE | TOTAL | normalized | 1.024324 | 0.979883 | 0.995793 | 1.005480 | 0.988120 | 1.170333 | 1.020196 | 1.058305 | 1.012988 | 1.015740 | 1.029011 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 23 | Montenegro | ME | TOTAL | normalized | 0.999019 | 0.994684 | 1.006297 | 1.003200 | 1.018684 | 1.138065 | 1.411355 | 1.088052 | 0.977652 | 0.974413 | 1.013372 | nan | nan | nan | nan |
| 24 | Hungary | HU | TOTAL | normalized | 1.026484 | 0.967604 | 1.005912 | 1.000787 | 0.990451 | 1.097414 | 1.187944 | 1.041669 | 0.978737 | 0.968079 | 0.996162 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 25 | Sweden | SE | TOTAL | normalized | 1.019326 | 0.985579 | 0.995095 | 0.995218 | 0.956506 | 1.079097 | 0.985779 | 1.011546 | 1.005714 | 0.969874 | 0.995711 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 26 | Croatia | HR | TOTAL | normalized | 1.036057 | 0.963041 | 1.000902 | 0.987618 | 0.969749 | 1.088706 | 1.174273 | 1.066778 | 0.959584 | 0.946901 | 0.991087 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 27 | Latvia | LV | TOTAL | normalized | 1.009782 | 0.991345 | 0.998873 | 1.001801 | 0.963078 | 1.022469 | 1.201443 | 1.055301 | 0.962590 | 0.912575 | 0.976822 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 28 | Bulgaria | BG | TOTAL | normalized | 1.024011 | 0.977541 | 0.998447 | 0.988264 | 0.984702 | 1.150005 | 1.357649 | 1.080035 | 0.919335 | 0.911709 | 0.970360 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 29 | Lithuania | LT | TOTAL | normalized | 1.034162 | 0.993484 | 0.972354 | 0.959272 | 0.928441 | 1.077686 | 1.154059 | 1.030467 | 0.919153 | 0.914776 | 0.954799 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 30 | Serbia | RS | TOTAL | normalized | 1.024670 | 0.974227 | 1.001103 | 0.980439 | 0.979254 | 1.155481 | 1.318381 | 1.013430 | 0.917367 | 0.925909 | 0.952236 | nan | nan | nan | nan |
| 31 | Romania | RO | TOTAL | normalized | 1.020584 | 0.981718 | 0.997698 | 1.005356 | 0.993947 | 1.151277 | 1.276275 | 1.026653 | 0.918109 | 0.837779 | 0.927514 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 32 | Armenia | AM | TOTAL | normalized | 1.008985 | 1.007475 | 0.983540 | 0.920898 | 0.919641 | 1.278886 | 1.266846 | 0.964385 | 0.873495 | 0.916035 | 0.917971 | nan | nan | nan | nan |
Lets sort by the vaccines¶
| name | abbr | filter_x | value_type_x | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | average(2022-2024) | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | Finland | FI | TOTAL | normalized | 0.996378 | 1.003504 | 1.000118 | 1.016952 | 1.004682 | 1.051873 | 1.076956 | 1.155757 | 1.143393 | 1.078209 | 1.125786 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 8 | Portugal | PT | TOTAL | normalized | 1.006119 | 1.000327 | 0.993554 | 1.024547 | 1.013474 | 1.140328 | 1.128946 | 1.127418 | 1.071651 | 1.067896 | 1.088988 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 21 | Italy | IT | TOTAL | normalized | 1.028396 | 0.960289 | 1.011315 | 0.983592 | 0.989873 | 1.165225 | 1.087305 | 1.095037 | 1.013642 | 0.986208 | 1.031629 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 12 | France | FR | TOTAL | normalized | 1.007603 | 0.985137 | 1.007261 | 1.013398 | 1.019252 | 1.131754 | 1.099201 | 1.122302 | 1.061615 | 1.062469 | 1.082129 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 25 | Sweden | SE | TOTAL | normalized | 1.019326 | 0.985579 | 0.995095 | 0.995218 | 0.956506 | 1.079097 | 0.985779 | 1.011546 | 1.005714 | 0.969874 | 0.995711 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 29 | Lithuania | LT | TOTAL | normalized | 1.034162 | 0.993484 | 0.972354 | 0.959272 | 0.928441 | 1.077686 | 1.154059 | 1.030467 | 0.919153 | 0.914776 | 0.954799 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 18 | Poland | PL | TOTAL | normalized | 1.011934 | 0.974416 | 1.013649 | 1.035756 | 1.026469 | 1.222837 | 1.306178 | 1.125759 | 1.027167 | 1.015246 | 1.056057 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 22 | Belgium | BE | TOTAL | normalized | 1.024324 | 0.979883 | 0.995793 | 1.005480 | 0.988120 | 1.170333 | 1.020196 | 1.058305 | 1.012988 | 1.015740 | 1.029011 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 7 | Denmark | DK | TOTAL | normalized | 1.007454 | 0.991352 | 1.001194 | 1.038393 | 1.014429 | 1.045972 | 1.075440 | 1.117541 | 1.090769 | 1.064901 | 1.091070 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 0 | Malta | MT | TOTAL | normalized | 1.016949 | 0.955701 | 1.027350 | 1.060863 | 1.064330 | 1.199538 | 1.196071 | 1.219183 | 1.161980 | 1.183648 | 1.188270 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 10 | Norway | NO | TOTAL | normalized | 1.016140 | 0.989495 | 0.994364 | 0.996615 | 0.991477 | 1.008311 | 1.026049 | 1.115550 | 1.065612 | 1.072463 | 1.084542 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 5 | Germany | DE | TOTAL | normalized | 1.016481 | 0.979311 | 1.004209 | 1.029001 | 1.012227 | 1.082065 | 1.101127 | 1.149954 | 1.106922 | 1.079127 | 1.112001 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 16 | Spain | ES | TOTAL | normalized | 1.023104 | 0.971158 | 1.005738 | 1.013880 | 0.992168 | 1.192895 | 1.070740 | 1.101128 | 1.039692 | 1.042102 | 1.060974 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 3 | Netherlands | NL | TOTAL | normalized | 1.003373 | 0.993167 | 1.003460 | 1.024649 | 1.015107 | 1.147749 | 1.140424 | 1.133556 | 1.131250 | 1.143136 | 1.135981 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 6 | Austria | AT | TOTAL | normalized | 1.023795 | 0.972870 | 1.003336 | 1.012615 | 1.006371 | 1.125312 | 1.104853 | 1.130013 | 1.081778 | 1.067871 | 1.093221 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 2 | Iceland | IS | TOTAL | normalized | 0.988146 | 1.020151 | 0.991702 | 1.000148 | 1.005927 | 1.042377 | 1.039265 | 1.195733 | 1.140169 | 1.153060 | 1.162987 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 13 | Greece | EL | TOTAL | normalized | 1.012210 | 0.969465 | 1.018325 | 0.984534 | 1.022175 | 1.091374 | 1.179188 | 1.151479 | 1.047889 | 1.026689 | 1.075352 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 1 | Cyprus | CY | TOTAL | normalized | 1.029186 | 0.937156 | 1.033658 | 0.997362 | 1.069610 | 1.143922 | 1.241800 | 1.239908 | 1.150459 | 1.124312 | 1.171560 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 9 | Luxembourg | LU | TOTAL | normalized | 0.992076 | 0.966098 | 1.041827 | 1.053835 | 1.050895 | 1.143779 | 1.100400 | 1.089127 | 1.081529 | 1.095744 | 1.088800 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 15 | Liechtenstein | LI | TOTAL | normalized | 0.997423 | 1.043814 | 0.958763 | 1.055412 | 1.005155 | 1.256443 | 1.032216 | 1.078608 | 1.020619 | 1.117268 | 1.072165 | vaccine | All | ratio (total_dose/pop.) | 1.815763 |
| 20 | Czechia | CZ | TOTAL | normalized | 1.024453 | 0.970711 | 1.004837 | 1.019293 | 1.014188 | 1.190285 | 1.259588 | 1.084875 | 1.016894 | 1.003669 | 1.035146 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 24 | Hungary | HU | TOTAL | normalized | 1.026484 | 0.967604 | 1.005912 | 1.000787 | 0.990451 | 1.097414 | 1.187944 | 1.041669 | 0.978737 | 0.968079 | 0.996162 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 17 | Estonia | EE | TOTAL | normalized | 1.003717 | 0.992890 | 1.003393 | 1.016879 | 0.994057 | 1.041062 | 1.194009 | 1.115234 | 1.034320 | 1.030883 | 1.060146 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 27 | Latvia | LV | TOTAL | normalized | 1.009782 | 0.991345 | 0.998873 | 1.001801 | 0.963078 | 1.022469 | 1.201443 | 1.055301 | 0.962590 | 0.912575 | 0.976822 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 26 | Croatia | HR | TOTAL | normalized | 1.036057 | 0.963041 | 1.000902 | 0.987618 | 0.969749 | 1.088706 | 1.174273 | 1.066778 | 0.959584 | 0.946901 | 0.991087 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 11 | Slovenia | SI | TOTAL | normalized | 1.005897 | 0.975647 | 1.018456 | 1.018057 | 1.023988 | 1.216257 | 1.150672 | 1.118378 | 1.070087 | 1.061216 | 1.083227 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 19 | Slovakia | SK | TOTAL | normalized | 1.023092 | 0.973345 | 1.003563 | 1.011692 | 0.992593 | 1.126210 | 1.363524 | 1.109279 | 1.008254 | 0.995303 | 1.037612 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 31 | Romania | RO | TOTAL | normalized | 1.020584 | 0.981718 | 0.997698 | 1.005356 | 0.993947 | 1.151277 | 1.276275 | 1.026653 | 0.918109 | 0.837779 | 0.927514 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 28 | Bulgaria | BG | TOTAL | normalized | 1.024011 | 0.977541 | 0.998447 | 0.988264 | 0.984702 | 1.150005 | 1.357649 | 1.080035 | 0.919335 | 0.911709 | 0.970360 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 14 | Switzerland | CH | TOTAL | normalized | 1.031079 | 0.969066 | 0.999855 | 1.002628 | 1.012027 | 1.137130 | 1.058255 | 1.110659 | 1.069182 | 1.037839 | 1.072560 | nan | nan | nan | nan |
| 23 | Montenegro | ME | TOTAL | normalized | 0.999019 | 0.994684 | 1.006297 | 1.003200 | 1.018684 | 1.138065 | 1.411355 | 1.088052 | 0.977652 | 0.974413 | 1.013372 | nan | nan | nan | nan |
| 30 | Serbia | RS | TOTAL | normalized | 1.024670 | 0.974227 | 1.001103 | 0.980439 | 0.979254 | 1.155481 | 1.318381 | 1.013430 | 0.917367 | 0.925909 | 0.952236 | nan | nan | nan | nan |
| 32 | Armenia | AM | TOTAL | normalized | 1.008985 | 1.007475 | 0.983540 | 0.920898 | 0.919641 | 1.278886 | 1.266846 | 0.964385 | 0.873495 | 0.916035 | 0.917971 | nan | nan | nan | nan |
# deaths values with out the NA
d = dxv.copy().dropna(subset=['value'])
d = d.sort_values(by='average(2022-2024)',ascending=True)
d = d.reset_index()
# vaccines values with out the NA
v = dxv.copy().dropna(subset=['value'])
v = v.sort_values(by='value',ascending=True)
v = v.reset_index()
v['rank'] = v['value'].rank()
# making sure the lists have the same values
d = d[d.abbr.isin(v.abbr)]
d = d.reset_index(drop=True)
# display(d)
# display(v)
# Create a plot
fig, ax = plt.subplots(figsize=(14, 7))
ydistance = 0.95
# Plot points for the first dataframe
for i, row in d.iterrows():
ax.scatter(0, i * ydistance, label=row['name'], color='blue')
ax.text(-0.025, i * ydistance, row['name'] + ': ' + f"{row['average(2022-2024)']:.2f}", ha='right', va='center', color='blue')
# Plot points for the second dataframe
for i, row in v.iterrows():
ax.scatter(1, i * ydistance, label=row['name'], color='red')
ax.text(1.025, i * ydistance, row['name'] + ': ' + f"{row['value']:.2f}", ha='left', va='center', color='red')
ax.text(-0.025 - 1.0, len(v) * ydistance, 'most deaths')
ax.text(-0.025 - 1.0, 0 * ydistance, 'least deaths')
ax.text(1.025 + 1.0, len(v) * ydistance, 'most vaccines')
ax.text(1.025 + 1.0, 0 * ydistance, 'least vaccines')
# Create a colormap
colormap = plt.get_cmap('magma')
norm = Normalize(vmin=-np.pi, vmax=np.pi)
# Draw lines connecting matching abbr values
for name in d['name']:
if name in v['name'].values:
idx1 = d[d['name'] == name].index[0] * ydistance
idx2 = v[v['name'] == name].index[0] * ydistance
ax.plot([0, 1], [idx1, idx2], color='gray', linestyle='-')
vertical_diff = idx2 - idx1
horizontal_diff = 1 # The horizontal distance between the columns
angle = np.arctan2(vertical_diff, horizontal_diff) # Calculate the angle
# normalized_angle = (angle + 2 * np.pi) / (2 * np.pi ) # Normalize angle to [0, 1]
normalized_angle = pow ( abs(angle / np.pi)/0.5, 3) # custom normalized angle for better colors
# print(normalized_angle)
color = colormap(normalized_angle) # Map normalized angle to a color
ax.plot([0, 1], [idx1, idx2], color=color, linestyle='-')
# Formatting
ax.set_xlim(-0.5, 1.5)
ax.set_ylim(-1, max(len(d), len(v)))
ax.set_xticks([0, 1])
ax.set_xticklabels(['deaths average(2022-2024)', 'vaccines ratio (total_dose/pop.)'])
ax.set_yticks([])
ax.set_title('Mapping Between deaths and vaccines\n High values at the top, Low values at the bottom')
ax.grid(False)
plt.tight_layout()
plt.show()
Spearman and Kendall¶
# create a ranked list for deaths - average(2022-2024)
d = dxv.copy().reset_index(drop=True)
d = d.sort_values(by='average(2022-2024)',ascending=False) #most deaths to least deaths
d = d[['name','abbr','average(2022-2024)']]
d['rank'] = d['average(2022-2024)'].rank()
# create a ranked list for vaccines - ratio (total_dose/pop.)
v = dxv.copy().reset_index(drop=True)
v = v.sort_values(by='value',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','value']]
v['rank'] = v['value'].rank()
# make sure the have the same countries
d = d[d.abbr.isin(v.abbr)]
for i, row in v.iterrows():
d.loc[d['abbr']==row['abbr'], 'rank'] = row['rank']
# no NAN values
d = d[~d['rank'].isna()]
v = v[~v['rank'].isna()]
display(MD(
'''### Output Interpretation
| Value | Effect | Description/Correlation-Suggestion |
|-----------|-----------------------------|------------------------------------------------------------|
| **1.0** | Positive effect | Suggests more vaccines will lead to fewer deaths. |
| **0.5** | Slightly positive effect | Slightly suggests more vaccines will lead to fewer deaths. |
| **0.0** | Null/No effect | Vaccines do not change the number of deaths. |
| **-0.5** | Slightly negative effect | Slightly suggests more vaccines will lead to more deaths. |
| **-1.0** | Negative effect | Suggests more vaccines will lead to more deaths. |
'''
))
# Calculate Spearman's Rank Correlation
spearman_corr, _ = spearmanr(d['rank'], v['rank'])
display(MD(f"### Spearman's Rank Correlation: {spearman_corr}"))
display(bar(((spearman_corr+1.0)/2.0)*100,length=100))
display('-1'+' '*48 + '0' + ' '*50 + '1')
# Calculate Kendall's Tau
kendall_corr, _ = kendalltau(d['rank'], v['rank'])
display(MD(f"### Kendall's Tau: {kendall_corr}"))
display(bar(((kendall_corr+1.0)/2.0)*100,length=100))
display('-1'+' '*48 + '0' + ' '*50 + '1')
# display(d)
# display(v)
Output Interpretation¶
| Value | Effect | Description/Correlation-Suggestion |
|---|---|---|
| 1.0 | Positive effect | Suggests more vaccines will lead to fewer deaths. |
| 0.5 | Slightly positive effect | Slightly suggests more vaccines will lead to fewer deaths. |
| 0.0 | Null/No effect | Vaccines do not change the number of deaths. |
| -0.5 | Slightly negative effect | Slightly suggests more vaccines will lead to more deaths. |
| -1.0 | Negative effect | Suggests more vaccines will lead to more deaths. |
Spearman's Rank Correlation: -0.3177339901477832¶
'[##################################__________________________________________________________________]'
'-1 0 1'
Kendall's Tau: -0.2364532019704433¶
'[######################################______________________________________________________________]'
'-1 0 1'
Spearman and Kendall ... continued.¶
what agegroups can we filter by to get the max Spearman and Kendall Values?
display(MD(
'''### Output Interpretation
| Value | Effect | Description/Correlation-Suggestion |
|-----------|-----------------------------|------------------------------------------------------------|
| **1.0** | Positive effect | Suggests more vaccines will lead to fewer deaths. |
| **0.5** | Slightly positive effect | Slightly suggests more vaccines will lead to fewer deaths. |
| **0.0** | Null/No effect | Vaccines do not change the number of deaths. |
| **-0.5** | Slightly negative effect | Slightly suggests more vaccines will lead to more deaths. |
| **-1.0** | Negative effect | Suggests more vaccines will lead to more deaths. |
'''
))
## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]
agegroups = ['Y_LT20','Y20-39', 'Y40-59', 'Y60-79', 'Y_GE80', ]
result = []
for ag in agegroups:
temp = cd[(cd['source']=='deaths') & (cd['filter'].isin([ag])) & (cd['value_type']=='normalized')]
temp = pd.pivot_table(
data = temp,
values = 'value',
index = ['abbr','name','filter','value_type'],
columns=['year'],
aggfunc='mean',
)
temp.columns.name = 'index'
temp = temp.reset_index()
temp['average(2022-2024)'] = (temp[2022]+temp[2023]+ temp[2024])/3.0
temp = temp.sort_values(by='average(2022-2024)',ascending=False)
temp = pd.merge(temp,v2023, how='left',on='name')
# create a ranked list for deaths - average(2022-2024)
d = temp.copy().reset_index(drop=True)
d = d.sort_values(by='average(2022-2024)',ascending=False) #most deaths to least deaths
d = d[['name','abbr','average(2022-2024)']]
d['rank'] = d['average(2022-2024)'].rank()
# create a ranked list for vaccines - ratio (total_dose/pop.)
v = temp.copy().reset_index(drop=True)
v = v.sort_values(by='value',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','value']]
v['rank'] = v['value'].rank()
# make sure the have the same countries
d = d[d.abbr.isin(v.abbr)]
for i, row in v.iterrows():
d.loc[d['abbr']==row['abbr'], 'rank'] = row['rank']
# no NAN values
d = d[~d['rank'].isna()]
v = v[~v['rank'].isna()]
# # Calculate Spearman's Rank Correlation
spearman_corr, _ = spearmanr(d['rank'], v['rank'])
# # Calculate Kendall's Tau
kendall_corr, _ = kendalltau(d['rank'], v['rank'])
result.append(
{
"agegroup":ag,
"spearman_corr":spearman_corr,
"kendall_corr":kendall_corr,
}
)
r = pd.DataFrame(result)
# r = r.sort_values(by='spearman_corr',ascending=False)
display(r.style.background_gradient(cmap=heatmapCM2, axis=0, subset=['spearman_corr']))
Output Interpretation¶
| Value | Effect | Description/Correlation-Suggestion |
|---|---|---|
| 1.0 | Positive effect | Suggests more vaccines will lead to fewer deaths. |
| 0.5 | Slightly positive effect | Slightly suggests more vaccines will lead to fewer deaths. |
| 0.0 | Null/No effect | Vaccines do not change the number of deaths. |
| -0.5 | Slightly negative effect | Slightly suggests more vaccines will lead to more deaths. |
| -1.0 | Negative effect | Suggests more vaccines will lead to more deaths. |
| agegroup | spearman_corr | kendall_corr | |
|---|---|---|---|
| 0 | Y_LT20 | -0.185714 | -0.123153 |
| 1 | Y20-39 | -0.320197 | -0.192118 |
| 2 | Y40-59 | 0.029064 | 0.014778 |
| 3 | Y60-79 | -0.340394 | -0.226601 |
| 4 | Y_GE80 | -0.230542 | -0.172414 |
Other death x vaccine charts¶
temp = dxv[dxv.value < 14] #gets rid of the outliar (finland)
temp = temp.dropna(subset=['value'])
# kde
g = sns.jointplot(
data=temp,
x="value",
y="average(2022-2024)",
kind="kde",
fill=True,
cmap=heatmapCM
)
# Add scatter points on top
g.ax_joint.scatter(
temp["value"],
temp['average(2022-2024)'],
color="black",
alpha=0.7,
s=7
)
g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))","Deaths (average(2022-2024))")
g.figure.suptitle("Kde Plot : Death x Vaccines", fontsize=14)
# Add a title
# plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)
# Add vertical and horizontal lines
plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1) # Vertical line at x=20
plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1) # Horizontal line at y=3
# display(vdravg)
plt.show()
lets look at Deaths for each year 2015-2024 separately¶
so the vaccine values will stay the same, but we are looking at the deaths overtime
temp = dxv[dxv.value < 14] #gets rid of the outliar (finland)
temp = temp.dropna(subset=['value'])
for y in [2015,2016,2017,2018,2019,2020,2021,2022,2023,2024]:
# kde
g = sns.jointplot(
data=temp,
x="value",
y=y,
kind="kde",
fill=True,
cmap=heatmapCM
)
# Add scatter points on top
g.ax_joint.scatter(
temp["value"],
temp[y],
color="black",
alpha=0.7,
s=7
)
g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))",f"Deaths ({y})")
g.figure.suptitle(f"Kde Plot : Death({y}) x Vaccines", fontsize=14)
# Add a title
# plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)
# Add vertical and horizontal lines
plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1) # Vertical line at x=20
plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1) # Horizontal line at y=3
# display(vdravg)
plt.show()
notes:¶
2015-2019
- provides the baseline
2020
- the vaccine wasn't rolled out until the very end of this year, so the vaccines should have very little effect in here.
2021
- this suggests that more vacccines caused less deaths.
- i.e. dots move down when looking at them from left, to right. ↘️
2022
- here it starts to change it's lean.
2023
- here we can see a shift that more that the countries with more vaccines start to experience more deaths
- i.e. dots move up when looking at them from left, to right. ↗️
2024
- and this is similar to 2023
- i.e. dots move up when looking at them from left, to right. ↗️
and in 2022,2023,2024...we can see how they might average the to be the graph seen earlier.
title = 'Scatter Chart - Death x Vaccine'
display(MD(f'### {title}'))
fig = px.scatter(
dxv,
x="value",
y="average(2022-2024)",
color="name",
hover_name="name",
title=title,
labels={
"value": "Vaccines (ratio total_dose/pop.)", # Custom X-axis label
"average(2022-2024)": "Deaths average(2022-2024)" # Custom Y-axis label
},
height=750
)
fig.update_layout(template="plotly_dark")
fig.add_shape(
type="line",
x0=0, x1=dxv.value.max(),
y0=1, y1=1, # Start and end y-coordinates
line=dict(color="Red", width=2, dash="dash"), # Line style
xref="x", yref="y" # Reference axes
)
fig.show()
Scatter Chart - Death x Vaccine¶
Bad-Batch Theory¶
There seems to be to be an unusual grouping/cluster¶
Where countries that have a vaccine (ratio total_dose/pop.) between 4.0-6.0, have an unusually high Deaths average(2022-2024) above 1.1.
Bad-Batch Theory (by Vibeke Manniche)¶
A study in European Journal of Clinical Investigation found significant batch-dependent variations in adverse event reports for the Pfizer-BioNTech COVID-19 vaccine in Denmark (2020–2022),
suggesting some batches had higher reported risks. Further investigation is needed.
The Paper
An Interview




my 2 cents¶
This is not suprising, the vaccine was created in a rushed manner ... and as a rule of thumb, Haste Makes Waste
- Ford Pinto (1971-1980)
- Windows Vista
- Samsung Galaxy Note 7
- Boeing 737 MAX
- ... and so many more
Lets look at some of the Causes of Death¶
note:
this data only does to 2023-ish.
with this limited amount of post-pandemic data
... it will be difficult to draw any meaningful conclusions.
title = 'Line Chart - normalized causes of death in Europe (by year)'
display(MD(f'### {title}'))
temp = cd[(cd['source']=='cause_of_death') & (~(cd['filter']=='TOTAL')) & (cd['value_type']=='normalized')]
temp = pd.pivot_table(
data = temp,
values = 'value',
index = ['filter'],
columns=['year'],
aggfunc='mean',
)
temp = temp.reset_index()
# temp['average(2021-2022)'] = (temp[2021]+temp[2022])/2.0
years = [i for i in range(2015,2023)]
# temp['stdev'] = temp[[2020,2021,2022]].std(axis=1)
# temp = temp.sort_values(by='stdev', ascending=False)
temp = temp.sort_values(by=2022, ascending=False)
display(temp.style.background_gradient(cmap=heatmapCM,axis=1,subset=years))
Line Chart - normalized causes of death in Europe (by year)¶
| year | filter | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 |
|---|---|---|---|---|---|---|---|---|---|---|
| 7 | Diseases of the skin and subcutaneous tissue (L00-L99) | 0.952700 | 0.988986 | 1.058314 | 1.075503 | 1.193232 | 1.258299 | 1.297455 | 1.591110 | 0.319839 |
| 12 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 0.995540 | 0.984867 | 1.019594 | 1.057114 | 1.219432 | 1.518470 | 1.339570 | 1.476779 | 0.290196 |
| 10 | Mental and behavioural disorders (F00-F99) | 0.931040 | 0.976116 | 1.092844 | 1.178312 | 1.225431 | 1.289635 | 1.347351 | 1.396606 | 0.221782 |
| 8 | Endocrine, nutritional and metabolic diseases (E00-E90) | 0.973747 | 0.979490 | 1.046763 | 1.070362 | 1.107944 | 1.249651 | 1.314197 | 1.272323 | 0.225376 |
| 4 | Diseases of the genitourinary system (N00-N99) | 0.992709 | 0.980498 | 1.026793 | 1.055125 | 1.045159 | 1.131820 | 1.178126 | 1.237250 | 0.273888 |
| 5 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 0.995449 | 0.958319 | 1.046231 | 1.105399 | 1.118685 | 1.068406 | 1.097093 | 1.139465 | 0.204976 |
| 3 | Diseases of the digestive system (K00-K93) | 0.988721 | 0.996419 | 1.014860 | 1.013569 | 1.038691 | 1.060206 | 1.109400 | 1.122434 | 0.222949 |
| 6 | Diseases of the respiratory system (J00-J99) | 0.991490 | 0.960160 | 1.048350 | 1.064795 | 1.023761 | 0.980327 | 0.985675 | 1.059276 | 0.222002 |
| 9 | Malignant neoplasms (C00-C97) | 0.994287 | 1.002868 | 1.002845 | 1.009227 | 1.008936 | 1.008924 | 0.992634 | 0.995533 | 0.196705 |
| 2 | Diseases of the circulatory system (I00-I99) | 1.021408 | 0.986229 | 0.992363 | 0.971170 | 0.947074 | 0.966720 | 0.980255 | 0.969827 | 0.181271 |
| 1 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 0.964858 | 1.030858 | 1.004284 | 1.007788 | 1.009398 | 0.988465 | 0.957862 | 0.950077 | 0.179165 |
| 11 | Pregnancy, childbirth and the puerperium (O00-O99) | 0.921207 | 1.079135 | 0.999658 | 0.881887 | 0.953946 | 0.962505 | 1.116414 | 0.928818 | 0.202308 |
| 0 | Certain conditions originating in the perinatal period (P00-P96) | 0.987427 | 1.015856 | 0.996718 | 0.953695 | 0.955849 | 0.880505 | 0.889766 | 0.851655 | 0.183221 |
Hypothesizing Adverse Reactions from an mRNA Vaccine¶
Blood Issues
- Since the vaccine is injected into the body, potential blood-related adverse reactions should be considered.
Heart Issues
- Based on reports and concerns about myocarditis and pericarditis.
DNA / Cancer Concerns (AKA Neoplasm)
Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs
- A study suggests mRNA vaccines may suppress innate immunity by affecting type I interferon signaling, potentially increasing the risk of neurodegenerative diseases, myocarditis, immune disorders, and cancer.
- Source: Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs
- From: National Library of Medicine
- Authors: Stephanie Seneff, Greg Nigh, Anthony M. Kyriakopoulos, Peter A. McCullough
- A study suggests mRNA vaccines may suppress innate immunity by affecting type I interferon signaling, potentially increasing the risk of neurodegenerative diseases, myocarditis, immune disorders, and cancer.
B-cell lymphoblastic lymphoma following intravenous BNT162b2 mRNA booster in a BALB/c mouse: A case report
- A research group reported the sudden death of a mouse after its second Pfizer/BioNTech mRNA vaccine dose, which was found to have B-cell lymphoblastic lymphoma (fast-growing blood cancer).
- Source: B-cell lymphoblastic lymphoma following intravenous BNT162b2 mRNA booster in a BALB/c mouse: A case report
- From: frontiersin.org
- Authors: Sander Eens, Manon Van Hecke , Kasper Favere , Thomas Tousseyn, Pieter-Jan Guns, Tania Roskams, Hein Heidbuchel
note: this has an addendum
- "In conclusion, the novel COVID-19 vaccines have demonstrated an exceptional benefit-risk ratio in the fight against the pandemic and manifestations of severe adverse reactions following COVID-19 vaccination are rare."
- however - the benefit-risk ratio value was not given.
- "In conclusion, the novel COVID-19 vaccines have demonstrated an exceptional benefit-risk ratio in the fight against the pandemic and manifestations of severe adverse reactions following COVID-19 vaccination are rare."
- A research group reported the sudden death of a mouse after its second Pfizer/BioNTech mRNA vaccine dose, which was found to have B-cell lymphoblastic lymphoma (fast-growing blood cancer).
DNA fragments detected in monovalent and bivalent 1Pfizer/BioNTech and Moderna modRNA COVID-19 vaccines 2from Ontario, Canada: Exploratory doseresponse 3relationship with serious adverse events.
- These data demonstrate the presence of billions to hundreds of billions of DNA 537molecules per dosein the modRNA COVID-19 products tested.
- Source: DNA fragments detected in monovalent and bivalent 1Pfizer/BioNTech and Moderna modRNA COVID-19 vaccines 2from Ontario, Canada: Exploratory doseresponse 3relationship with serious adverse events.
- Authors: David J. Speicher, Jessica Rose,L. Maria Gutschi,David Wiseman, Kevin McKernan
- These data demonstrate the presence of billions to hundreds of billions of DNA 537molecules per dosein the modRNA COVID-19 products tested.
COVID-19 Vaccines in People with Cancer
- "There are some other types of vaccines that might not be safe for some people with cancer, but this depends on many factors, such as the type of vaccine, the type of cancer a person has (had), if they're still being treated for cancer, and if their immune system is working properly. Because of this, it’s best to talk with your doctor before getting any type of vaccine."
- Source: COVID-19 Vaccines in People with Cancer
- From: cancer.org
- "There are some other types of vaccines that might not be safe for some people with cancer, but this depends on many factors, such as the type of vaccine, the type of cancer a person has (had), if they're still being treated for cancer, and if their immune system is working properly. Because of this, it’s best to talk with your doctor before getting any type of vaccine."
Unknown Issues
- As a new medication, unforeseen adverse reactions may arise.
Spearman and Kendall ... (again)¶
# create a ranked list for vaccines - ratio (total_dose/pop.)
v = dxv.copy().reset_index(drop=True)
v = v.rename(columns={'value': 'vaccines | ratio (total_dose/pop.)'})
v = v[~v['vaccines | ratio (total_dose/pop.)'].isna()]
v = v.sort_values(by='vaccines | ratio (total_dose/pop.)',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','vaccines | ratio (total_dose/pop.)']]
v['rank'] = v['vaccines | ratio (total_dose/pop.)'].rank()
# display(v)
cods = cd[(cd.source == 'cause_of_death') & (cd.value_type == 'normalized') & (cd['year'] == 2022) & (~cd.value.isna()) & (~(cd['filter'] == 'Total')) ]['filter'].drop_duplicates().to_list()
result = []
for c in cods:
# print(c)
temp = pd.pivot_table(
data = cd[(cd['filter'] == c) & (cd.value_type == 'normalized') & (cd['year'] == 2022)],
values = 'value',
index = ['name','abbr'],
columns=['year'],
aggfunc='mean',
)
temp = temp.reset_index()
temp = temp.sort_values(by=2022,ascending=False) #most deaths to least deaths
temp['rank'] = temp[2022].rank()
# display(MD(f'#### {c}'))
# display(temp)
temp = temp[temp.abbr.isin(v.abbr)]
vtemp = v[v.abbr.isin(temp.abbr)]
for i, row in vtemp.iterrows():
temp.loc[temp['abbr']==row['abbr'], 'rank'] = row['rank']
result.append(
{
'cause_of_death': c,
'Spearman_Rank': spearmanr(temp['rank'], vtemp['rank'])[0],
'Kendall_Tau': kendalltau(temp['rank'], vtemp['rank'])[0]
}
)
# display(temp)
# display(vtemp)
# display(MD(f"## {c} x Vaccines"))
# # Calculate Spearman's Rank Correlation
# spearman_corr, _ = spearmanr(temp['rank'], vtemp['rank'])
# display(MD(f"### Spearman's Rank Correlation: {spearman_corr}"))
# display(bar(((spearman_corr+1.0)/2.0)*100,length=100))
# display('-1'+' '*48 + '0' + ' '*50 + '1')
# # Calculate Kendall's Tau
# kendall_corr, _ = kendalltau(temp['rank'], v['rank'])
# display(MD(f"### Kendall's Tau: {kendall_corr}"))
# display( bar(((kendall_corr+1.0)/2.0)*100,length=100))
# display('-1'+' '*48 + '0' + ' '*50 + '1')
display(MD(
'''### Output Interpretation
| Value | Effect | Description/Correlation-Suggestion |
|-----------|-----------------------------|----------------------------------------------------------------------|
| **1.0** | Positive effect | Suggests more vaccines will lead to fewer [cause of death]. |
| **0.5** | Slightly positive effect | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| **0.0** | Null/No effect | Vaccines do not change the number of [cause of death]. |
| **-0.5** | Slightly negative effect | Slightly suggests more vaccines will lead to more [cause of death]. |
| **-1.0** | Negative effect | Suggests more vaccines will lead to more [cause of death]. |
'''
))
result = pd.DataFrame(result)
result = result.sort_values(by='Spearman_Rank')
result = result.reset_index(drop=True)
display(MD("#### lets point out how many causes of death's increased with the number of vaccines"))
threshold = 0.29
display(MD(f"* {len(result[result['Spearman_Rank'] <= threshold*-1.0])} with a Spearman_Rank less than -{threshold}"))
display(MD(f"* {len(result[result['Spearman_Rank'] >= threshold])} with a Spearman_Rank greater than {threshold}"))
display(result.style.background_gradient(cmap=heatmapCM4)) #,vmax=1.0,vmin=-1.0))
# lets dataframe the top and bottom causes of death
TB_cod = pd.concat([result[result['Spearman_Rank'] <= threshold*-1.0],result[result['Spearman_Rank'] >= threshold]])
Output Interpretation¶
| Value | Effect | Description/Correlation-Suggestion |
|---|---|---|
| 1.0 | Positive effect | Suggests more vaccines will lead to fewer [cause of death]. |
| 0.5 | Slightly positive effect | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| 0.0 | Null/No effect | Vaccines do not change the number of [cause of death]. |
| -0.5 | Slightly negative effect | Slightly suggests more vaccines will lead to more [cause of death]. |
| -1.0 | Negative effect | Suggests more vaccines will lead to more [cause of death]. |
lets point out how many causes of death's increased with the number of vaccines¶
- 2 with a Spearman_Rank less than -0.29
- 1 with a Spearman_Rank greater than 0.29
| cause_of_death | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|
| 0 | Malignant neoplasms (C00-C97) | -0.483853 | -0.317460 |
| 1 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | -0.447181 | -0.333333 |
| 2 | Pregnancy, childbirth and the puerperium (O00-O99) | -0.226957 | -0.195652 |
| 3 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | -0.182813 | -0.095238 |
| 4 | Mental and behavioural disorders (F00-F99) | -0.132458 | -0.068783 |
| 5 | Diseases of the genitourinary system (N00-N99) | -0.076628 | -0.026455 |
| 6 | Diseases of the digestive system (K00-K93) | -0.045977 | -0.031746 |
| 7 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | -0.045430 | -0.058201 |
| 8 | Endocrine, nutritional and metabolic diseases (E00-E90) | -0.015873 | 0.000000 |
| 9 | Certain conditions originating in the perinatal period (P00-P96) | 0.009852 | 0.015873 |
| 10 | Diseases of the skin and subcutaneous tissue (L00-L99) | 0.023536 | 0.037037 |
| 11 | Diseases of the circulatory system (I00-I99) | 0.145594 | 0.079365 |
| 12 | Diseases of the respiratory system (J00-J99) | 0.295567 | 0.211640 |
Note:¶
- Pregnancy, childbirth and the puerperium (O00-O99) only effects women (~50% of the population), so we should see that as double ... so account for that.
- the more vaccines the less Diseases of the respiratory system (J00-J99), however the spearman rank is low.
- Malignant neoplasms (C00-C97) has the highest absolute spearman ranking.
Lets see the Spearmans Rank over time¶
# create a ranked list for vaccines - ratio (total_dose/pop.)
v = dxv.copy().reset_index(drop=True)
v = v.rename(columns={'value': 'vaccines | ratio (total_dose/pop.)'})
v = v[~v['vaccines | ratio (total_dose/pop.)'].isna()]
v = v.sort_values(by='vaccines | ratio (total_dose/pop.)',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','vaccines | ratio (total_dose/pop.)']]
v['rank'] = v['vaccines | ratio (total_dose/pop.)'].rank()
# display(v)
cods = cd[(cd.source == 'cause_of_death') & (cd.value_type == 'normalized') & (cd['year'] == 2022) & (~cd.value.isna()) & (~(cd['filter'] == 'Total')) ]['filter'].drop_duplicates().to_list()
years = [i for i in range(2015,2023,1)]
result = []
for c in cods:
for y in years:
temp = pd.pivot_table(
data = cd[(cd['filter'] == c) & (cd.value_type == 'normalized') & (cd['year'] == y)],
values = 'value',
index = ['name','abbr'],
columns=['year'],
aggfunc='mean',
)
temp = temp.reset_index()
temp = temp.sort_values(by=y,ascending=False) #most deaths to least deaths
temp['rank'] = temp[y].rank()
# display(temp)
temp = temp[temp.abbr.isin(v.abbr)]
vtemp = v[v.abbr.isin(temp.abbr)]
for i, row in vtemp.iterrows():
temp.loc[temp['abbr']==row['abbr'], 'rank'] = row['rank']
result.append(
{
'cause_of_death': c,
'year': y,
'Spearman_Rank': spearmanr(temp['rank'], vtemp['rank'])[0],
'Kendall_Tau': kendalltau(temp['rank'], vtemp['rank'])[0]
}
)
display(MD(
'''### Output Interpretation
| Value | Effect | Description/Correlation-Suggestion |
|-----------|-----------------------------|----------------------------------------------------------------------|
| **1.0** | Positive effect | Suggests more vaccines will lead to fewer [cause of death]. |
| **0.5** | Slightly positive effect | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| **0.0** | Null/No effect | Vaccines do not change the number of [cause of death]. |
| **-0.5** | Slightly negative effect | Slightly suggests more vaccines will lead to more [cause of death]. |
| **-1.0** | Negative effect | Suggests more vaccines will lead to more [cause of death]. |
'''
))
result = pd.DataFrame(result)
result = result.sort_values(by='Spearman_Rank')
result = result.reset_index(drop=True)
display(MD("#### lets point out how many causes of death's increased with the number of vaccines"))
display(MD(f"* {len(result[result['Spearman_Rank'] <= -0.3])} with a Spearman_Rank less than -0.3"))
display(MD(f"* {len(result[result['Spearman_Rank'] >= 0.3])} with a Spearman_Rank greater than 0.3"))
for y in years:
display(MD(f'##### year = {y}'))
display(result[result['year']==y].style.background_gradient(cmap=heatmapCM4,subset=['Spearman_Rank','Kendall_Tau'])) #,vmax=1.0,vmin=-1.0))
Output Interpretation¶
| Value | Effect | Description/Correlation-Suggestion |
|---|---|---|
| 1.0 | Positive effect | Suggests more vaccines will lead to fewer [cause of death]. |
| 0.5 | Slightly positive effect | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| 0.0 | Null/No effect | Vaccines do not change the number of [cause of death]. |
| -0.5 | Slightly negative effect | Slightly suggests more vaccines will lead to more [cause of death]. |
| -1.0 | Negative effect | Suggests more vaccines will lead to more [cause of death]. |
lets point out how many causes of death's increased with the number of vaccines¶
- 9 with a Spearman_Rank less than -0.3
- 8 with a Spearman_Rank greater than 0.3
year = 2015¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 8 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2015 | -0.317460 | -0.243386 |
| 11 | Diseases of the genitourinary system (N00-N99) | 2015 | -0.292830 | -0.190476 |
| 34 | Certain conditions originating in the perinatal period (P00-P96) | 2015 | -0.068418 | -0.063492 |
| 44 | Diseases of the circulatory system (I00-I99) | 2015 | -0.005473 | -0.015873 |
| 52 | Diseases of the digestive system (K00-K93) | 2015 | 0.022989 | 0.005291 |
| 56 | Diseases of the respiratory system (J00-J99) | 2015 | 0.035577 | 0.021164 |
| 58 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2015 | 0.047619 | 0.042328 |
| 59 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2015 | 0.056377 | 0.005291 |
| 61 | Mental and behavioural disorders (F00-F99) | 2015 | 0.084291 | 0.084656 |
| 65 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2015 | 0.092501 | 0.052910 |
| 81 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2015 | 0.152162 | 0.095238 |
| 91 | Malignant neoplasms (C00-C97) | 2015 | 0.235906 | 0.169312 |
| 92 | Pregnancy, childbirth and the puerperium (O00-O99) | 2015 | 0.241739 | 0.144928 |
year = 2016¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 9 | Diseases of the circulatory system (I00-I99) | 2016 | -0.293924 | -0.211640 |
| 10 | Malignant neoplasms (C00-C97) | 2016 | -0.293377 | -0.185185 |
| 12 | Diseases of the digestive system (K00-K93) | 2016 | -0.282430 | -0.185185 |
| 22 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2016 | -0.165846 | -0.111111 |
| 25 | Diseases of the respiratory system (J00-J99) | 2016 | -0.138478 | -0.132275 |
| 29 | Mental and behavioural disorders (F00-F99) | 2016 | -0.121511 | -0.132275 |
| 30 | Certain conditions originating in the perinatal period (P00-P96) | 2016 | -0.116585 | -0.084656 |
| 45 | Diseases of the genitourinary system (N00-N99) | 2016 | -0.003831 | 0.015873 |
| 46 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2016 | -0.001642 | -0.042328 |
| 57 | Pregnancy, childbirth and the puerperium (O00-O99) | 2016 | 0.039130 | 0.057971 |
| 60 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2016 | 0.082102 | 0.052910 |
| 82 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2016 | 0.157088 | 0.132275 |
| 86 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2016 | 0.206349 | 0.164021 |
year = 2017¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 6 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2017 | -0.333881 | -0.227513 |
| 13 | Pregnancy, childbirth and the puerperium (O00-O99) | 2017 | -0.254783 | -0.210145 |
| 23 | Mental and behavioural disorders (F00-F99) | 2017 | -0.153257 | -0.089947 |
| 35 | Malignant neoplasms (C00-C97) | 2017 | -0.062945 | -0.042328 |
| 37 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2017 | -0.055829 | -0.058201 |
| 38 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2017 | -0.053640 | -0.047619 |
| 63 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2017 | 0.085933 | 0.084656 |
| 74 | Diseases of the digestive system (K00-K93) | 2017 | 0.122605 | 0.063492 |
| 75 | Certain conditions originating in the perinatal period (P00-P96) | 2017 | 0.125889 | 0.105820 |
| 77 | Diseases of the respiratory system (J00-J99) | 2017 | 0.144499 | 0.089947 |
| 85 | Diseases of the genitourinary system (N00-N99) | 2017 | 0.198139 | 0.137566 |
| 97 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2017 | 0.347564 | 0.259259 |
| 102 | Diseases of the circulatory system (I00-I99) | 2017 | 0.407772 | 0.227513 |
year = 2018¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 2 | Pregnancy, childbirth and the puerperium (O00-O99) | 2018 | -0.426087 | -0.304348 |
| 16 | Certain conditions originating in the perinatal period (P00-P96) | 2018 | -0.210181 | -0.158730 |
| 17 | Mental and behavioural disorders (F00-F99) | 2018 | -0.200876 | -0.148148 |
| 19 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2018 | -0.191571 | -0.142857 |
| 21 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2018 | -0.170772 | -0.116402 |
| 27 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2018 | -0.131910 | -0.089947 |
| 51 | Malignant neoplasms (C00-C97) | 2018 | 0.018610 | -0.005291 |
| 66 | Diseases of the circulatory system (I00-I99) | 2018 | 0.096333 | 0.026455 |
| 71 | Diseases of the respiratory system (J00-J99) | 2018 | 0.105090 | 0.084656 |
| 76 | Diseases of the genitourinary system (N00-N99) | 2018 | 0.134100 | 0.074074 |
| 84 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2018 | 0.191024 | 0.095238 |
| 99 | Diseases of the digestive system (K00-K93) | 2018 | 0.377121 | 0.253968 |
| 100 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2018 | 0.381500 | 0.248677 |
year = 2019¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 4 | Pregnancy, childbirth and the puerperium (O00-O99) | 2019 | -0.365217 | -0.260870 |
| 14 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2019 | -0.254516 | -0.148148 |
| 18 | Malignant neoplasms (C00-C97) | 2019 | -0.199781 | -0.132275 |
| 28 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2019 | -0.126437 | -0.084656 |
| 50 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2019 | 0.014778 | 0.021164 |
| 54 | Diseases of the respiratory system (J00-J99) | 2019 | 0.024083 | -0.010582 |
| 55 | Mental and behavioural disorders (F00-F99) | 2019 | 0.032841 | 0.021164 |
| 67 | Diseases of the digestive system (K00-K93) | 2019 | 0.097975 | 0.074074 |
| 73 | Certain conditions originating in the perinatal period (P00-P96) | 2019 | 0.117132 | 0.095238 |
| 83 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2019 | 0.176793 | 0.111111 |
| 88 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2019 | 0.221675 | 0.148148 |
| 90 | Diseases of the genitourinary system (N00-N99) | 2019 | 0.230980 | 0.164021 |
| 94 | Diseases of the circulatory system (I00-I99) | 2019 | 0.282978 | 0.201058 |
year = 2020¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 7 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2020 | -0.329502 | -0.222222 |
| 31 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2020 | -0.087575 | -0.052910 |
| 33 | Diseases of the digestive system (K00-K93) | 2020 | -0.072250 | -0.037037 |
| 41 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2020 | -0.029009 | -0.010582 |
| 42 | Malignant neoplasms (C00-C97) | 2020 | -0.025725 | 0.026455 |
| 47 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2020 | 0.007115 | -0.037037 |
| 62 | Certain conditions originating in the perinatal period (P00-P96) | 2020 | 0.085386 | 0.047619 |
| 69 | Pregnancy, childbirth and the puerperium (O00-O99) | 2020 | 0.101739 | 0.057971 |
| 70 | Mental and behavioural disorders (F00-F99) | 2020 | 0.104543 | 0.063492 |
| 87 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2020 | 0.220033 | 0.164021 |
| 89 | Diseases of the respiratory system (J00-J99) | 2020 | 0.224412 | 0.164021 |
| 93 | Diseases of the genitourinary system (N00-N99) | 2020 | 0.245758 | 0.137566 |
| 96 | Diseases of the circulatory system (I00-I99) | 2020 | 0.315818 | 0.227513 |
year = 2021¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 3 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2021 | -0.392447 | -0.306878 |
| 5 | Malignant neoplasms (C00-C97) | 2021 | -0.357964 | -0.280423 |
| 24 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2021 | -0.143404 | -0.084656 |
| 36 | Certain conditions originating in the perinatal period (P00-P96) | 2021 | -0.056924 | -0.021164 |
| 48 | Diseases of the digestive system (K00-K93) | 2021 | 0.007115 | 0.015873 |
| 64 | Mental and behavioural disorders (F00-F99) | 2021 | 0.091954 | 0.042328 |
| 68 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2021 | 0.098522 | 0.063492 |
| 72 | Pregnancy, childbirth and the puerperium (O00-O99) | 2021 | 0.110435 | 0.065217 |
| 79 | Diseases of the genitourinary system (N00-N99) | 2021 | 0.148878 | 0.100529 |
| 80 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2021 | 0.151067 | 0.089947 |
| 98 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2021 | 0.363437 | 0.291005 |
| 101 | Diseases of the circulatory system (I00-I99) | 2021 | 0.403394 | 0.285714 |
| 103 | Diseases of the respiratory system (J00-J99) | 2021 | 0.451560 | 0.338624 |
year = 2022¶
| cause_of_death | year | Spearman_Rank | Kendall_Tau | |
|---|---|---|---|---|
| 0 | Malignant neoplasms (C00-C97) | 2022 | -0.483853 | -0.317460 |
| 1 | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | 2022 | -0.447181 | -0.333333 |
| 15 | Pregnancy, childbirth and the puerperium (O00-O99) | 2022 | -0.226957 | -0.195652 |
| 20 | Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99) | 2022 | -0.182813 | -0.095238 |
| 26 | Mental and behavioural disorders (F00-F99) | 2022 | -0.132458 | -0.068783 |
| 32 | Diseases of the genitourinary system (N00-N99) | 2022 | -0.076628 | -0.026455 |
| 39 | Diseases of the digestive system (K00-K93) | 2022 | -0.045977 | -0.031746 |
| 40 | Diseases of the musculoskeletal system and connective tissue (M00-M99) | 2022 | -0.045430 | -0.058201 |
| 43 | Endocrine, nutritional and metabolic diseases (E00-E90) | 2022 | -0.015873 | 0.000000 |
| 49 | Certain conditions originating in the perinatal period (P00-P96) | 2022 | 0.009852 | 0.015873 |
| 53 | Diseases of the skin and subcutaneous tissue (L00-L99) | 2022 | 0.023536 | 0.037037 |
| 78 | Diseases of the circulatory system (I00-I99) | 2022 | 0.145594 | 0.079365 |
| 95 | Diseases of the respiratory system (J00-J99) | 2022 | 0.295567 | 0.211640 |
title = 'Line Chart - Cause of Death x Spearmans Rank'
display(MD(f'### {title}'))
temp = result.copy()
temp = temp.sort_values(by='Spearman_Rank', ascending=True)
temp = temp.sort_values(by='year', ascending=True)
fig = px.line(
temp,
x='year',
y='Spearman_Rank',
color='cause_of_death',
height=750 ,
title=title
)
fig.update_layout(template="plotly_dark")
Line Chart - Cause of Death x Spearmans Rank¶
Note:¶
in the Line Chart - Cause of Death x Spearmans Rank chart above, from 2021-2022 there are many Causes of Death going in the downward (in the negative direction).
I'm wondering if this will continue.
lets see these overtime¶
# lets also filter by the top 10 most and least 10 vaccinated countries
v = dxv.copy().reset_index(drop=True)
v = v.sort_values(by='value',ascending=False) #least vaccine to most vaccines
v = v[~v['value'].isna()]
# include_abbr = pd.concat([v.head(5),v.tail(5)]).abbr.to_list()
include_abbr = v.abbr.drop_duplicates().to_list()
# let's print this lists for reference
# display(v.head(5))
# display(v.tail(5))
for index,row in TB_cod.iterrows():
print(index,row)
temp = cd[(cd['source']=='cause_of_death') & (cd['filter'] == row['cause_of_death']) & (cd['value_type']=='normalized') & (cd['year'] < 2023)]
temp = temp[temp['abbr'].isin(include_abbr)]
## adding the vaccine value (ratio (total_dose/pop.))
temp['vac dose/pop.'] = 0.0
for i, vrow in v.iterrows():
mask = temp['abbr'] == vrow['abbr'] # Create a boolean mask
temp.loc[mask, 'vac dose/pop.'] = vrow['value']
temp = temp.sort_values(by=['year', 'name'], ascending=[True, True])
fig = px.line(
temp,
x='year',
y='value',
color='name',
height=750 ,
hover_data={
'name', 'abbr', 'value','vac dose/pop.'
},
title=f"cause_of_death \'{row['cause_of_death']}\' , Spearman_Rank {row['Spearman_Rank']}"
)
fig.update_layout(template="plotly_dark")
fig.add_shape(
type="line",
x0=2015, x1=2022,
y0=1, y1=1, # Start and end y-coordinates
line=dict(color="Red", width=2, dash="dash"), # Line style
xref="x", yref="y" # Reference axes
)
fig.show()
0 cause_of_death Malignant neoplasms (C00-C97) Spearman_Rank -0.484 Kendall_Tau -0.317 Name: 0, dtype: object
1 cause_of_death Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) Spearman_Rank -0.447 Kendall_Tau -0.333 Name: 1, dtype: object
12 cause_of_death Diseases of the respiratory system (J00-J99) Spearman_Rank 0.296 Kendall_Tau 0.212 Name: 12, dtype: object
temp = cd[(cd['source']=='cause_of_death') & (cd['value_type']=='normalized')]
codlist = ['Malignant neoplasms (C00-C97)','Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)','Diseases of the respiratory system (J00-J99)']
temp = temp[temp['filter'].isin(codlist)]
temp = pd.pivot_table(
data = temp,
values = 'value',
index = ['abbr','name','filter','value_type'],
columns=['year'],
aggfunc='mean',
)
temp.columns.name = 'index'
temp = temp.reset_index()
## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]
# adding it to the temp
temp = pd.merge(temp,v2023, how='left',on='name')
for c in codlist:
display(MD(f'### {c}'))
temp2 = temp[temp['filter_x']==c]
temp2 = temp2.sort_values(by=2022,ascending=False)
styled_df = temp2.style.background_gradient(cmap=heatmapCM2, axis=1, subset=[i for i in range(2015,2023)]) \
.background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
display(MD('#### sorted by 2022 column'))
display(styled_df)
temp2 = temp2.sort_values(by='value',ascending=False)
styled_df = temp2.style.background_gradient(cmap=heatmapCM2, axis=1, subset=[i for i in range(2015,2023)]) \
.background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
display(MD('#### sorted by value (ratio (total_dose/pop.))'))
display(styled_df)
# we will use this in a cell below
codxv = temp.copy()
Malignant neoplasms (C00-C97)¶
sorted by 2022 column¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 14 | CY | Cyprus | Malignant neoplasms (C00-C97) | normalized | 0.991532 | 0.951183 | 1.057285 | 1.068493 | 1.072229 | 1.133499 | 1.188045 | 1.117808 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 47 | IE | Ireland | Malignant neoplasms (C00-C97) | normalized | 0.979477 | 1.011917 | 1.008606 | 1.021516 | 1.056383 | 1.064548 | 1.063886 | 1.114421 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 65 | MT | Malta | Malignant neoplasms (C00-C97) | normalized | 0.988223 | 0.994647 | 1.017131 | 1.119914 | 1.016060 | 1.077088 | 1.046039 | 1.099572 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 50 | IS | Iceland | Malignant neoplasms (C00-C97) | normalized | 0.964978 | 1.084591 | 0.950431 | 0.934267 | 1.021552 | 1.005388 | 0.995690 | 1.087823 | 1.076509 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 35 | FI | Finland | Malignant neoplasms (C00-C97) | normalized | 0.980161 | 1.006497 | 1.013343 | 1.012135 | 1.039357 | 1.047572 | 1.060377 | 1.053854 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 2 | AT | Austria | Malignant neoplasms (C00-C97) | normalized | 1.004749 | 0.995967 | 0.999284 | 1.013433 | 1.012262 | 1.031582 | 1.018751 | 1.041828 | 1.038022 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 71 | NO | Norway | Malignant neoplasms (C00-C97) | normalized | 0.991960 | 1.001995 | 1.006045 | 1.005861 | 0.999417 | 1.002639 | 1.007702 | 1.041121 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 77 | PT | Portugal | Malignant neoplasms (C00-C97) | normalized | 0.980787 | 1.006920 | 1.012293 | 1.027973 | 1.050609 | 1.045051 | 1.017483 | 1.028120 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 32 | ES | Spain | Malignant neoplasms (C00-C97) | normalized | 0.989611 | 1.003440 | 1.006949 | 1.001899 | 1.005047 | 1.001963 | 1.009764 | 1.021535 | 1.027951 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 68 | NL | Netherlands | Malignant neoplasms (C00-C97) | normalized | 0.986301 | 1.011021 | 1.002677 | 0.999473 | 1.007528 | 1.006549 | 1.006104 | 1.018831 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 17 | CZ | Czechia | Malignant neoplasms (C00-C97) | normalized | 0.989232 | 1.004760 | 1.006008 | 1.019701 | 1.038570 | 1.032917 | 0.996867 | 1.016911 | 1.006669 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 89 | SI | Slovenia | Malignant neoplasms (C00-C97) | normalized | 0.990301 | 0.996979 | 1.012721 | 1.042455 | 0.997138 | 1.021784 | 0.986961 | 1.015742 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 20 | DE | Germany | Malignant neoplasms (C00-C97) | normalized | 0.992060 | 1.010891 | 0.997049 | 1.007774 | 1.013348 | 1.012863 | 1.002733 | 1.014257 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 23 | DK | Denmark | Malignant neoplasms (C00-C97) | normalized | 0.987781 | 1.011301 | 1.000919 | 1.003931 | 1.032706 | 1.011749 | 1.031360 | 1.011621 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 38 | FR | France | Malignant neoplasms (C00-C97) | normalized | 0.991905 | 1.004069 | 1.004026 | 0.997865 | 1.002613 | 1.001065 | 0.995754 | 1.007122 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 11 | CH | Switzerland | Malignant neoplasms (C00-C97) | normalized | 1.001197 | 0.997778 | 1.001026 | 1.005698 | 0.997493 | 0.977379 | 0.973789 | 0.995840 | 0.000000 | nan | nan | nan | nan |
| 86 | SE | Sweden | Malignant neoplasms (C00-C97) | normalized | 0.989690 | 0.996485 | 1.013825 | 0.995911 | 0.993396 | 0.997676 | 0.980910 | 0.993352 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 5 | BE | Belgium | Malignant neoplasms (C00-C97) | normalized | 1.008534 | 1.008903 | 0.982563 | 0.977000 | 0.983594 | 0.967091 | 0.965801 | 0.971511 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 56 | LT | Lithuania | Malignant neoplasms (C00-C97) | normalized | 1.019847 | 1.002132 | 0.978020 | 0.980727 | 0.982572 | 1.003855 | 0.948864 | 0.970147 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 53 | IT | Italy | Malignant neoplasms (C00-C97) | normalized | 0.999683 | 0.999372 | 1.000945 | 1.001978 | 0.994882 | 0.987206 | 0.969412 | 0.967980 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 29 | EL | Greece | Malignant neoplasms (C00-C97) | normalized | 0.993818 | 1.006138 | 1.000045 | 0.995358 | 1.004229 | 1.010891 | 1.007577 | 0.966031 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 74 | PL | Poland | Malignant neoplasms (C00-C97) | normalized | 1.005447 | 0.998825 | 0.995728 | 1.012948 | 1.002550 | 0.997746 | 0.935670 | 0.960480 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 59 | LU | Luxembourg | Malignant neoplasms (C00-C97) | normalized | 0.979083 | 0.997539 | 1.023377 | 0.993848 | 1.018763 | 0.939403 | 0.979083 | 0.958782 | 0.977238 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 62 | LV | Latvia | Malignant neoplasms (C00-C97) | normalized | 0.991956 | 0.996512 | 1.011532 | 1.002250 | 1.000225 | 1.012544 | 0.971367 | 0.958204 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 92 | SK | Slovakia | Malignant neoplasms (C00-C97) | normalized | 0.997125 | 0.998441 | 1.004434 | 1.026214 | 0.991863 | 1.033742 | 0.951958 | 0.941214 | 0.971472 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 41 | HR | Croatia | Malignant neoplasms (C00-C97) | normalized | 1.005125 | 1.010202 | 0.984673 | 1.001335 | 0.966725 | 0.952065 | 0.957858 | 0.935618 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 44 | HU | Hungary | Malignant neoplasms (C00-C97) | normalized | 0.997496 | 1.003427 | 0.999077 | 0.991229 | 0.973769 | 0.961936 | 0.930604 | 0.926437 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 8 | BG | Bulgaria | Malignant neoplasms (C00-C97) | normalized | 1.025194 | 0.983432 | 0.991373 | 0.992287 | 1.036849 | 1.051645 | 0.977034 | 0.925674 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 26 | EE | Estonia | Malignant neoplasms (C00-C97) | normalized | 1.010756 | 0.987146 | 1.002099 | 1.025184 | 0.998951 | 0.957765 | 0.969307 | 0.913431 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 83 | RS | Serbia | Malignant neoplasms (C00-C97) | normalized | 0.996101 | 1.003138 | 1.000761 | 1.006913 | 0.994470 | 0.967767 | 0.931046 | 0.901734 | 0.000000 | nan | nan | nan | nan |
| 80 | RO | Romania | Malignant neoplasms (C00-C97) | normalized | 0.992785 | 1.003276 | 1.003938 | 1.000454 | 0.971862 | 0.961682 | 0.893871 | 0.884509 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
sorted by value (ratio (total_dose/pop.))¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 35 | FI | Finland | Malignant neoplasms (C00-C97) | normalized | 0.980161 | 1.006497 | 1.013343 | 1.012135 | 1.039357 | 1.047572 | 1.060377 | 1.053854 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 77 | PT | Portugal | Malignant neoplasms (C00-C97) | normalized | 0.980787 | 1.006920 | 1.012293 | 1.027973 | 1.050609 | 1.045051 | 1.017483 | 1.028120 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 53 | IT | Italy | Malignant neoplasms (C00-C97) | normalized | 0.999683 | 0.999372 | 1.000945 | 1.001978 | 0.994882 | 0.987206 | 0.969412 | 0.967980 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 38 | FR | France | Malignant neoplasms (C00-C97) | normalized | 0.991905 | 1.004069 | 1.004026 | 0.997865 | 1.002613 | 1.001065 | 0.995754 | 1.007122 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 86 | SE | Sweden | Malignant neoplasms (C00-C97) | normalized | 0.989690 | 0.996485 | 1.013825 | 0.995911 | 0.993396 | 0.997676 | 0.980910 | 0.993352 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 56 | LT | Lithuania | Malignant neoplasms (C00-C97) | normalized | 1.019847 | 1.002132 | 0.978020 | 0.980727 | 0.982572 | 1.003855 | 0.948864 | 0.970147 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 74 | PL | Poland | Malignant neoplasms (C00-C97) | normalized | 1.005447 | 0.998825 | 0.995728 | 1.012948 | 1.002550 | 0.997746 | 0.935670 | 0.960480 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 5 | BE | Belgium | Malignant neoplasms (C00-C97) | normalized | 1.008534 | 1.008903 | 0.982563 | 0.977000 | 0.983594 | 0.967091 | 0.965801 | 0.971511 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 47 | IE | Ireland | Malignant neoplasms (C00-C97) | normalized | 0.979477 | 1.011917 | 1.008606 | 1.021516 | 1.056383 | 1.064548 | 1.063886 | 1.114421 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 23 | DK | Denmark | Malignant neoplasms (C00-C97) | normalized | 0.987781 | 1.011301 | 1.000919 | 1.003931 | 1.032706 | 1.011749 | 1.031360 | 1.011621 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 65 | MT | Malta | Malignant neoplasms (C00-C97) | normalized | 0.988223 | 0.994647 | 1.017131 | 1.119914 | 1.016060 | 1.077088 | 1.046039 | 1.099572 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 71 | NO | Norway | Malignant neoplasms (C00-C97) | normalized | 0.991960 | 1.001995 | 1.006045 | 1.005861 | 0.999417 | 1.002639 | 1.007702 | 1.041121 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 20 | DE | Germany | Malignant neoplasms (C00-C97) | normalized | 0.992060 | 1.010891 | 0.997049 | 1.007774 | 1.013348 | 1.012863 | 1.002733 | 1.014257 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 32 | ES | Spain | Malignant neoplasms (C00-C97) | normalized | 0.989611 | 1.003440 | 1.006949 | 1.001899 | 1.005047 | 1.001963 | 1.009764 | 1.021535 | 1.027951 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 68 | NL | Netherlands | Malignant neoplasms (C00-C97) | normalized | 0.986301 | 1.011021 | 1.002677 | 0.999473 | 1.007528 | 1.006549 | 1.006104 | 1.018831 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 2 | AT | Austria | Malignant neoplasms (C00-C97) | normalized | 1.004749 | 0.995967 | 0.999284 | 1.013433 | 1.012262 | 1.031582 | 1.018751 | 1.041828 | 1.038022 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 50 | IS | Iceland | Malignant neoplasms (C00-C97) | normalized | 0.964978 | 1.084591 | 0.950431 | 0.934267 | 1.021552 | 1.005388 | 0.995690 | 1.087823 | 1.076509 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 29 | EL | Greece | Malignant neoplasms (C00-C97) | normalized | 0.993818 | 1.006138 | 1.000045 | 0.995358 | 1.004229 | 1.010891 | 1.007577 | 0.966031 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 14 | CY | Cyprus | Malignant neoplasms (C00-C97) | normalized | 0.991532 | 0.951183 | 1.057285 | 1.068493 | 1.072229 | 1.133499 | 1.188045 | 1.117808 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 59 | LU | Luxembourg | Malignant neoplasms (C00-C97) | normalized | 0.979083 | 0.997539 | 1.023377 | 0.993848 | 1.018763 | 0.939403 | 0.979083 | 0.958782 | 0.977238 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 17 | CZ | Czechia | Malignant neoplasms (C00-C97) | normalized | 0.989232 | 1.004760 | 1.006008 | 1.019701 | 1.038570 | 1.032917 | 0.996867 | 1.016911 | 1.006669 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 44 | HU | Hungary | Malignant neoplasms (C00-C97) | normalized | 0.997496 | 1.003427 | 0.999077 | 0.991229 | 0.973769 | 0.961936 | 0.930604 | 0.926437 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 26 | EE | Estonia | Malignant neoplasms (C00-C97) | normalized | 1.010756 | 0.987146 | 1.002099 | 1.025184 | 0.998951 | 0.957765 | 0.969307 | 0.913431 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 62 | LV | Latvia | Malignant neoplasms (C00-C97) | normalized | 0.991956 | 0.996512 | 1.011532 | 1.002250 | 1.000225 | 1.012544 | 0.971367 | 0.958204 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 41 | HR | Croatia | Malignant neoplasms (C00-C97) | normalized | 1.005125 | 1.010202 | 0.984673 | 1.001335 | 0.966725 | 0.952065 | 0.957858 | 0.935618 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 89 | SI | Slovenia | Malignant neoplasms (C00-C97) | normalized | 0.990301 | 0.996979 | 1.012721 | 1.042455 | 0.997138 | 1.021784 | 0.986961 | 1.015742 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 92 | SK | Slovakia | Malignant neoplasms (C00-C97) | normalized | 0.997125 | 0.998441 | 1.004434 | 1.026214 | 0.991863 | 1.033742 | 0.951958 | 0.941214 | 0.971472 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 80 | RO | Romania | Malignant neoplasms (C00-C97) | normalized | 0.992785 | 1.003276 | 1.003938 | 1.000454 | 0.971862 | 0.961682 | 0.893871 | 0.884509 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 8 | BG | Bulgaria | Malignant neoplasms (C00-C97) | normalized | 1.025194 | 0.983432 | 0.991373 | 0.992287 | 1.036849 | 1.051645 | 0.977034 | 0.925674 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 11 | CH | Switzerland | Malignant neoplasms (C00-C97) | normalized | 1.001197 | 0.997778 | 1.001026 | 1.005698 | 0.997493 | 0.977379 | 0.973789 | 0.995840 | 0.000000 | nan | nan | nan | nan |
| 83 | RS | Serbia | Malignant neoplasms (C00-C97) | normalized | 0.996101 | 1.003138 | 1.000761 | 1.006913 | 0.994470 | 0.967767 | 0.931046 | 0.901734 | 0.000000 | nan | nan | nan | nan |
Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)¶
sorted by 2022 column¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 12 | CY | Cyprus | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.933333 | 1.133333 | 0.933333 | 0.533333 | 1.666667 | 1.066667 | 1.400000 | 1.466667 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 45 | IE | Ireland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.049080 | 0.926380 | 1.024540 | 1.104294 | 1.233129 | 1.141104 | 1.098160 | 1.190184 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 18 | DE | Germany | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.925963 | 1.018763 | 1.055274 | 1.085700 | 1.061359 | 1.032454 | 1.081643 | 1.152637 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 87 | SI | Slovenia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.728972 | 1.205607 | 1.065421 | 1.261682 | 1.345794 | 1.065421 | 1.065421 | 1.149533 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 3 | BE | Belgium | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.947814 | 1.095910 | 0.956276 | 1.315938 | 1.121298 | 1.002821 | 0.875882 | 1.108604 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 75 | PT | Portugal | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.049734 | 0.953819 | 0.996448 | 1.246892 | 1.278863 | 1.087034 | 1.134991 | 1.076377 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 33 | FI | Finland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.886239 | 1.078899 | 1.034862 | 0.963303 | 0.935780 | 0.941284 | 1.056881 | 1.073394 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 30 | ES | Spain | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.977254 | 1.030869 | 0.991877 | 1.027214 | 0.993095 | 1.002843 | 1.015028 | 1.050366 | 1.012591 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 9 | CH | Switzerland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.030508 | 1.010169 | 0.959322 | 0.847458 | 0.888136 | 0.830508 | 0.820339 | 1.040678 | 0.000000 | nan | nan | nan | nan |
| 84 | SE | Sweden | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.807259 | 1.073842 | 1.118899 | 1.122653 | 0.987484 | 1.025031 | 0.976220 | 1.040050 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 0 | AT | Austria | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.008816 | 1.031486 | 0.959698 | 1.027708 | 1.005038 | 1.008816 | 0.944584 | 1.039043 | 1.031486 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 66 | NL | Netherlands | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.968590 | 0.975164 | 1.056245 | 1.130752 | 1.130752 | 0.981738 | 0.966399 | 1.010226 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 36 | FR | France | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.017804 | 1.008309 | 0.973887 | 0.900297 | 0.960237 | 0.897923 | 0.900890 | 0.999407 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 15 | CZ | Czechia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.977597 | 1.026477 | 0.995927 | 1.014257 | 0.916497 | 0.940937 | 0.910387 | 0.983707 | 0.837067 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 51 | IT | Italy | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.039006 | 0.918261 | 1.042733 | 0.980870 | 0.948820 | 1.005466 | 0.983106 | 0.975652 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 21 | DK | Denmark | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.946988 | 1.026506 | 1.026506 | 1.091566 | 0.932530 | 1.055422 | 0.881928 | 0.939759 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 72 | PL | Poland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.981561 | 0.970969 | 1.047470 | 1.043939 | 0.927423 | 0.915653 | 0.993331 | 0.939192 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 63 | MT | Malta | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.913043 | 0.847826 | 1.239130 | 1.173913 | 1.239130 | 1.173913 | 1.173913 | 0.913043 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 48 | IS | Iceland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.900000 | 1.050000 | 1.050000 | 0.450000 | 1.500000 | 1.950000 | 0.900000 | 0.900000 | 0.750000 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 81 | RS | Serbia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.918750 | 1.050000 | 1.031250 | 1.031250 | 1.068750 | 0.984375 | 0.900000 | 0.890625 | 0.000000 | nan | nan | nan | nan |
| 90 | SK | Slovakia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.042316 | 1.042316 | 0.915367 | 1.189310 | 1.155902 | 0.968820 | 0.855234 | 0.888641 | 0.761693 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 27 | EL | Greece | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.862559 | 1.127962 | 1.009479 | 0.872038 | 0.928910 | 1.071090 | 0.848341 | 0.886256 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 42 | HU | Hungary | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.025612 | 0.978842 | 0.995546 | 0.988864 | 0.881960 | 0.895323 | 0.841871 | 0.865256 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 69 | NO | Norway | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.916031 | 1.045802 | 1.038168 | 0.916031 | 0.977099 | 0.969466 | 1.022901 | 0.832061 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 78 | RO | Romania | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.047894 | 0.988439 | 0.963666 | 0.886870 | 0.775392 | 0.837325 | 0.797688 | 0.815029 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 39 | HR | Croatia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.088328 | 1.003155 | 0.908517 | 0.899054 | 0.690852 | 0.917981 | 0.908517 | 0.804416 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 24 | EE | Estonia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.759494 | 0.949367 | 1.291139 | 1.518987 | 0.797468 | 0.797468 | 0.835443 | 0.797468 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 57 | LU | Luxembourg | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.870968 | 1.258065 | 0.870968 | 1.064516 | 0.677419 | 0.870968 | 1.258065 | 0.774194 | 1.161290 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 54 | LT | Lithuania | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.098214 | 1.017857 | 0.883929 | 0.937500 | 0.723214 | 0.736607 | 0.790179 | 0.696429 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 6 | BG | Bulgaria | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.184416 | 0.989610 | 0.825974 | 0.763636 | 0.864935 | 0.732468 | 0.740260 | 0.592208 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 60 | LV | Latvia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.006452 | 1.122581 | 0.870968 | 0.851613 | 0.677419 | 0.735484 | 0.716129 | 0.561290 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
sorted by value (ratio (total_dose/pop.))¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 33 | FI | Finland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.886239 | 1.078899 | 1.034862 | 0.963303 | 0.935780 | 0.941284 | 1.056881 | 1.073394 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 75 | PT | Portugal | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.049734 | 0.953819 | 0.996448 | 1.246892 | 1.278863 | 1.087034 | 1.134991 | 1.076377 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 51 | IT | Italy | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.039006 | 0.918261 | 1.042733 | 0.980870 | 0.948820 | 1.005466 | 0.983106 | 0.975652 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 36 | FR | France | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.017804 | 1.008309 | 0.973887 | 0.900297 | 0.960237 | 0.897923 | 0.900890 | 0.999407 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 84 | SE | Sweden | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.807259 | 1.073842 | 1.118899 | 1.122653 | 0.987484 | 1.025031 | 0.976220 | 1.040050 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 54 | LT | Lithuania | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.098214 | 1.017857 | 0.883929 | 0.937500 | 0.723214 | 0.736607 | 0.790179 | 0.696429 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 72 | PL | Poland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.981561 | 0.970969 | 1.047470 | 1.043939 | 0.927423 | 0.915653 | 0.993331 | 0.939192 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 3 | BE | Belgium | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.947814 | 1.095910 | 0.956276 | 1.315938 | 1.121298 | 1.002821 | 0.875882 | 1.108604 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 45 | IE | Ireland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.049080 | 0.926380 | 1.024540 | 1.104294 | 1.233129 | 1.141104 | 1.098160 | 1.190184 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 21 | DK | Denmark | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.946988 | 1.026506 | 1.026506 | 1.091566 | 0.932530 | 1.055422 | 0.881928 | 0.939759 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 63 | MT | Malta | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.913043 | 0.847826 | 1.239130 | 1.173913 | 1.239130 | 1.173913 | 1.173913 | 0.913043 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 69 | NO | Norway | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.916031 | 1.045802 | 1.038168 | 0.916031 | 0.977099 | 0.969466 | 1.022901 | 0.832061 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 18 | DE | Germany | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.925963 | 1.018763 | 1.055274 | 1.085700 | 1.061359 | 1.032454 | 1.081643 | 1.152637 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 30 | ES | Spain | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.977254 | 1.030869 | 0.991877 | 1.027214 | 0.993095 | 1.002843 | 1.015028 | 1.050366 | 1.012591 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 66 | NL | Netherlands | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.968590 | 0.975164 | 1.056245 | 1.130752 | 1.130752 | 0.981738 | 0.966399 | 1.010226 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 0 | AT | Austria | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.008816 | 1.031486 | 0.959698 | 1.027708 | 1.005038 | 1.008816 | 0.944584 | 1.039043 | 1.031486 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 48 | IS | Iceland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.900000 | 1.050000 | 1.050000 | 0.450000 | 1.500000 | 1.950000 | 0.900000 | 0.900000 | 0.750000 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 27 | EL | Greece | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.862559 | 1.127962 | 1.009479 | 0.872038 | 0.928910 | 1.071090 | 0.848341 | 0.886256 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 12 | CY | Cyprus | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.933333 | 1.133333 | 0.933333 | 0.533333 | 1.666667 | 1.066667 | 1.400000 | 1.466667 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 57 | LU | Luxembourg | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.870968 | 1.258065 | 0.870968 | 1.064516 | 0.677419 | 0.870968 | 1.258065 | 0.774194 | 1.161290 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 15 | CZ | Czechia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.977597 | 1.026477 | 0.995927 | 1.014257 | 0.916497 | 0.940937 | 0.910387 | 0.983707 | 0.837067 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 42 | HU | Hungary | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.025612 | 0.978842 | 0.995546 | 0.988864 | 0.881960 | 0.895323 | 0.841871 | 0.865256 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 24 | EE | Estonia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.759494 | 0.949367 | 1.291139 | 1.518987 | 0.797468 | 0.797468 | 0.835443 | 0.797468 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 60 | LV | Latvia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.006452 | 1.122581 | 0.870968 | 0.851613 | 0.677419 | 0.735484 | 0.716129 | 0.561290 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 39 | HR | Croatia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.088328 | 1.003155 | 0.908517 | 0.899054 | 0.690852 | 0.917981 | 0.908517 | 0.804416 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 87 | SI | Slovenia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.728972 | 1.205607 | 1.065421 | 1.261682 | 1.345794 | 1.065421 | 1.065421 | 1.149533 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 90 | SK | Slovakia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.042316 | 1.042316 | 0.915367 | 1.189310 | 1.155902 | 0.968820 | 0.855234 | 0.888641 | 0.761693 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 78 | RO | Romania | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.047894 | 0.988439 | 0.963666 | 0.886870 | 0.775392 | 0.837325 | 0.797688 | 0.815029 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 6 | BG | Bulgaria | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.184416 | 0.989610 | 0.825974 | 0.763636 | 0.864935 | 0.732468 | 0.740260 | 0.592208 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 9 | CH | Switzerland | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 1.030508 | 1.010169 | 0.959322 | 0.847458 | 0.888136 | 0.830508 | 0.820339 | 1.040678 | 0.000000 | nan | nan | nan | nan |
| 81 | RS | Serbia | Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99) | normalized | 0.918750 | 1.050000 | 1.031250 | 1.031250 | 1.068750 | 0.984375 | 0.900000 | 0.890625 | 0.000000 | nan | nan | nan | nan |
Diseases of the respiratory system (J00-J99)¶
sorted by 2022 column¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 91 | SK | Slovakia | Diseases of the respiratory system (J00-J99) | normalized | 1.021516 | 0.890940 | 1.087544 | 1.026846 | 0.966147 | 1.181405 | 2.025563 | 1.594157 | 1.340111 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 64 | MT | Malta | Diseases of the respiratory system (J00-J99) | normalized | 0.986072 | 0.941504 | 1.072423 | 1.089136 | 1.284123 | 1.200557 | 1.253482 | 1.456825 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 79 | RO | Romania | Diseases of the respiratory system (J00-J99) | normalized | 0.997274 | 0.968615 | 1.034111 | 1.138241 | 1.181262 | 1.426691 | 1.592194 | 1.340648 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 7 | BG | Bulgaria | Diseases of the respiratory system (J00-J99) | normalized | 0.929681 | 1.018976 | 1.051343 | 1.120897 | 0.965261 | 1.379141 | 1.567144 | 1.282730 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 82 | RS | Serbia | Diseases of the respiratory system (J00-J99) | normalized | 1.055686 | 0.925837 | 1.018478 | 0.996646 | 1.044865 | 1.274758 | 1.366260 | 1.248560 | 0.000000 | nan | nan | nan | nan |
| 49 | IS | Iceland | Diseases of the respiratory system (J00-J99) | normalized | 0.836735 | 0.948980 | 1.214286 | 1.163265 | 0.857143 | 0.923469 | 0.933673 | 1.229592 | 1.040816 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 73 | PL | Poland | Diseases of the respiratory system (J00-J99) | normalized | 0.989726 | 0.938055 | 1.072220 | 1.123850 | 1.109924 | 1.171204 | 1.147222 | 1.226214 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 25 | EE | Estonia | Diseases of the respiratory system (J00-J99) | normalized | 0.980036 | 1.007260 | 1.012704 | 1.194192 | 1.088929 | 0.932849 | 1.121597 | 1.136116 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 16 | CZ | Czechia | Diseases of the respiratory system (J00-J99) | normalized | 0.988986 | 0.945151 | 1.065862 | 1.101009 | 1.085739 | 1.097718 | 1.027424 | 1.127731 | 1.140632 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 61 | LV | Latvia | Diseases of the respiratory system (J00-J99) | normalized | 0.898739 | 1.037395 | 1.063866 | 1.162185 | 0.984454 | 0.901261 | 0.983193 | 1.123109 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 13 | CY | Cyprus | Diseases of the respiratory system (J00-J99) | normalized | 1.003663 | 0.906593 | 1.089744 | 0.974359 | 1.203297 | 1.139194 | 0.954212 | 1.082418 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 37 | FR | France | Diseases of the respiratory system (J00-J99) | normalized | 0.995234 | 0.962637 | 1.042129 | 1.050499 | 1.059961 | 0.901281 | 0.846249 | 1.049755 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 22 | DK | Denmark | Diseases of the respiratory system (J00-J99) | normalized | 0.965601 | 0.985910 | 1.048489 | 1.118829 | 1.008366 | 0.934724 | 0.987726 | 1.038417 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 67 | NL | Netherlands | Diseases of the respiratory system (J00-J99) | normalized | 1.010924 | 0.955243 | 1.033833 | 1.120297 | 1.006788 | 0.837121 | 0.807451 | 1.033515 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 1 | AT | Austria | Diseases of the respiratory system (J00-J99) | normalized | 0.950587 | 0.923174 | 1.126239 | 1.178745 | 1.127504 | 1.029873 | 0.852534 | 1.027553 | 1.148380 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 52 | IT | Italy | Diseases of the respiratory system (J00-J99) | normalized | 0.980644 | 0.940604 | 1.078753 | 1.046090 | 1.084513 | 1.154365 | 0.914167 | 1.024463 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 10 | CH | Switzerland | Diseases of the respiratory system (J00-J99) | normalized | 1.036089 | 0.920944 | 1.042967 | 1.036311 | 1.022112 | 0.841739 | 0.808238 | 1.010353 | 0.000000 | nan | nan | nan | nan |
| 19 | DE | Germany | Diseases of the respiratory system (J00-J99) | normalized | 1.018797 | 0.960947 | 1.020256 | 1.069720 | 1.000015 | 0.914894 | 0.854677 | 1.008996 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 70 | NO | Norway | Diseases of the respiratory system (J00-J99) | normalized | 0.959046 | 0.991522 | 1.049432 | 1.019001 | 0.995155 | 0.855715 | 0.828236 | 0.999016 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 4 | BE | Belgium | Diseases of the respiratory system (J00-J99) | normalized | 1.008991 | 0.955891 | 1.035118 | 1.108343 | 1.041629 | 0.884865 | 0.778579 | 0.988275 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 46 | IE | Ireland | Diseases of the respiratory system (J00-J99) | normalized | 0.977738 | 0.995446 | 1.026815 | 1.024791 | 0.994182 | 0.827473 | 0.810018 | 0.980015 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 58 | LU | Luxembourg | Diseases of the respiratory system (J00-J99) | normalized | 1.036364 | 0.956150 | 1.007487 | 1.244920 | 1.241711 | 1.039572 | 0.959358 | 0.972193 | 1.273797 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 85 | SE | Sweden | Diseases of the respiratory system (J00-J99) | normalized | 0.989235 | 0.969670 | 1.041095 | 1.083795 | 0.959267 | 0.862378 | 0.788624 | 0.962838 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 40 | HR | Croatia | Diseases of the respiratory system (J00-J99) | normalized | 0.968126 | 0.890546 | 1.141328 | 0.857349 | 0.841833 | 0.807914 | 0.910031 | 0.961270 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 34 | FI | Finland | Diseases of the respiratory system (J00-J99) | normalized | 0.944544 | 1.039681 | 1.015775 | 1.088958 | 0.961132 | 0.854285 | 0.860628 | 0.946495 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 28 | EL | Greece | Diseases of the respiratory system (J00-J99) | normalized | 1.047398 | 0.974959 | 0.977642 | 0.894401 | 0.959568 | 0.887906 | 0.923419 | 0.929491 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 76 | PT | Portugal | Diseases of the respiratory system (J00-J99) | normalized | 1.016271 | 1.016573 | 0.967155 | 1.003823 | 0.923698 | 0.849835 | 0.774992 | 0.916681 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 55 | LT | Lithuania | Diseases of the respiratory system (J00-J99) | normalized | 1.020121 | 0.968813 | 1.011066 | 1.044266 | 0.906942 | 0.908451 | 0.802062 | 0.876761 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 43 | HU | Hungary | Diseases of the respiratory system (J00-J99) | normalized | 1.069291 | 0.906061 | 1.024648 | 1.020246 | 1.045649 | 0.900528 | 0.847208 | 0.864185 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 31 | ES | Spain | Diseases of the respiratory system (J00-J99) | normalized | 1.035062 | 0.934527 | 1.030411 | 1.071775 | 0.951875 | 0.846907 | 0.709559 | 0.858905 | 0.938340 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 88 | SI | Slovenia | Diseases of the respiratory system (J00-J99) | normalized | 1.018009 | 0.986365 | 0.995626 | 0.835863 | 0.833548 | 0.622074 | 0.520196 | 0.540262 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
sorted by value (ratio (total_dose/pop.))¶
| abbr | name | filter_x | value_type_x | 2015.000000 | 2016.000000 | 2017.000000 | 2018.000000 | 2019.000000 | 2020.000000 | 2021.000000 | 2022.000000 | 2023.000000 | source | filter_y | value_type_y | value | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 34 | FI | Finland | Diseases of the respiratory system (J00-J99) | normalized | 0.944544 | 1.039681 | 1.015775 | 1.088958 | 0.961132 | 0.854285 | 0.860628 | 0.946495 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 6.993552 |
| 76 | PT | Portugal | Diseases of the respiratory system (J00-J99) | normalized | 1.016271 | 1.016573 | 0.967155 | 1.003823 | 0.923698 | 0.849835 | 0.774992 | 0.916681 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 5.294853 |
| 52 | IT | Italy | Diseases of the respiratory system (J00-J99) | normalized | 0.980644 | 0.940604 | 1.078753 | 1.046090 | 1.084513 | 1.154365 | 0.914167 | 1.024463 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.765223 |
| 37 | FR | France | Diseases of the respiratory system (J00-J99) | normalized | 0.995234 | 0.962637 | 1.042129 | 1.050499 | 1.059961 | 0.901281 | 0.846249 | 1.049755 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.584656 |
| 85 | SE | Sweden | Diseases of the respiratory system (J00-J99) | normalized | 0.989235 | 0.969670 | 1.041095 | 1.083795 | 0.959267 | 0.862378 | 0.788624 | 0.962838 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 4.336310 |
| 55 | LT | Lithuania | Diseases of the respiratory system (J00-J99) | normalized | 1.020121 | 0.968813 | 1.011066 | 1.044266 | 0.906942 | 0.908451 | 0.802062 | 0.876761 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 3.106103 |
| 73 | PL | Poland | Diseases of the respiratory system (J00-J99) | normalized | 0.989726 | 0.938055 | 1.072220 | 1.123850 | 1.109924 | 1.171204 | 1.147222 | 1.226214 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.862610 |
| 4 | BE | Belgium | Diseases of the respiratory system (J00-J99) | normalized | 1.008991 | 0.955891 | 1.035118 | 1.108343 | 1.041629 | 0.884865 | 0.778579 | 0.988275 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.411964 |
| 46 | IE | Ireland | Diseases of the respiratory system (J00-J99) | normalized | 0.977738 | 0.995446 | 1.026815 | 1.024791 | 0.994182 | 0.827473 | 0.810018 | 0.980015 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.405531 |
| 22 | DK | Denmark | Diseases of the respiratory system (J00-J99) | normalized | 0.965601 | 0.985910 | 1.048489 | 1.118829 | 1.008366 | 0.934724 | 0.987726 | 1.038417 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.402740 |
| 64 | MT | Malta | Diseases of the respiratory system (J00-J99) | normalized | 0.986072 | 0.941504 | 1.072423 | 1.089136 | 1.284123 | 1.200557 | 1.253482 | 1.456825 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.322112 |
| 70 | NO | Norway | Diseases of the respiratory system (J00-J99) | normalized | 0.959046 | 0.991522 | 1.049432 | 1.019001 | 0.995155 | 0.855715 | 0.828236 | 0.999016 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.212603 |
| 19 | DE | Germany | Diseases of the respiratory system (J00-J99) | normalized | 1.018797 | 0.960947 | 1.020256 | 1.069720 | 1.000015 | 0.914894 | 0.854677 | 1.008996 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.211555 |
| 31 | ES | Spain | Diseases of the respiratory system (J00-J99) | normalized | 1.035062 | 0.934527 | 1.030411 | 1.071775 | 0.951875 | 0.846907 | 0.709559 | 0.858905 | 0.938340 | vaccine | All | ratio (total_dose/pop.) | 2.196480 |
| 67 | NL | Netherlands | Diseases of the respiratory system (J00-J99) | normalized | 1.010924 | 0.955243 | 1.033833 | 1.120297 | 1.006788 | 0.837121 | 0.807451 | 1.033515 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.195485 |
| 1 | AT | Austria | Diseases of the respiratory system (J00-J99) | normalized | 0.950587 | 0.923174 | 1.126239 | 1.178745 | 1.127504 | 1.029873 | 0.852534 | 1.027553 | 1.148380 | vaccine | All | ratio (total_dose/pop.) | 2.141405 |
| 49 | IS | Iceland | Diseases of the respiratory system (J00-J99) | normalized | 0.836735 | 0.948980 | 1.214286 | 1.163265 | 0.857143 | 0.923469 | 0.933673 | 1.229592 | 1.040816 | vaccine | All | ratio (total_dose/pop.) | 2.138589 |
| 28 | EL | Greece | Diseases of the respiratory system (J00-J99) | normalized | 1.047398 | 0.974959 | 0.977642 | 0.894401 | 0.959568 | 0.887906 | 0.923419 | 0.929491 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 2.032484 |
| 13 | CY | Cyprus | Diseases of the respiratory system (J00-J99) | normalized | 1.003663 | 0.906593 | 1.089744 | 0.974359 | 1.203297 | 1.139194 | 0.954212 | 1.082418 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.988003 |
| 58 | LU | Luxembourg | Diseases of the respiratory system (J00-J99) | normalized | 1.036364 | 0.956150 | 1.007487 | 1.244920 | 1.241711 | 1.039572 | 0.959358 | 0.972193 | 1.273797 | vaccine | All | ratio (total_dose/pop.) | 1.914499 |
| 16 | CZ | Czechia | Diseases of the respiratory system (J00-J99) | normalized | 0.988986 | 0.945151 | 1.065862 | 1.101009 | 1.085739 | 1.097718 | 1.027424 | 1.127731 | 1.140632 | vaccine | All | ratio (total_dose/pop.) | 1.685242 |
| 43 | HU | Hungary | Diseases of the respiratory system (J00-J99) | normalized | 1.069291 | 0.906061 | 1.024648 | 1.020246 | 1.045649 | 0.900528 | 0.847208 | 0.864185 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.617727 |
| 25 | EE | Estonia | Diseases of the respiratory system (J00-J99) | normalized | 0.980036 | 1.007260 | 1.012704 | 1.194192 | 1.088929 | 0.932849 | 1.121597 | 1.136116 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.582880 |
| 61 | LV | Latvia | Diseases of the respiratory system (J00-J99) | normalized | 0.898739 | 1.037395 | 1.063866 | 1.162185 | 0.984454 | 0.901261 | 0.983193 | 1.123109 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.503200 |
| 40 | HR | Croatia | Diseases of the respiratory system (J00-J99) | normalized | 0.968126 | 0.890546 | 1.141328 | 0.857349 | 0.841833 | 0.807914 | 0.910031 | 0.961270 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.390181 |
| 88 | SI | Slovenia | Diseases of the respiratory system (J00-J99) | normalized | 1.018009 | 0.986365 | 0.995626 | 0.835863 | 0.833548 | 0.622074 | 0.520196 | 0.540262 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 1.379628 |
| 91 | SK | Slovakia | Diseases of the respiratory system (J00-J99) | normalized | 1.021516 | 0.890940 | 1.087544 | 1.026846 | 0.966147 | 1.181405 | 2.025563 | 1.594157 | 1.340111 | vaccine | All | ratio (total_dose/pop.) | 1.279721 |
| 79 | RO | Romania | Diseases of the respiratory system (J00-J99) | normalized | 0.997274 | 0.968615 | 1.034111 | 1.138241 | 1.181262 | 1.426691 | 1.592194 | 1.340648 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.817503 |
| 7 | BG | Bulgaria | Diseases of the respiratory system (J00-J99) | normalized | 0.929681 | 1.018976 | 1.051343 | 1.120897 | 0.965261 | 1.379141 | 1.567144 | 1.282730 | 0.000000 | vaccine | All | ratio (total_dose/pop.) | 0.666504 |
| 82 | RS | Serbia | Diseases of the respiratory system (J00-J99) | normalized | 1.055686 | 0.925837 | 1.018478 | 0.996646 | 1.044865 | 1.274758 | 1.366260 | 1.248560 | 0.000000 | nan | nan | nan | nan |
| 10 | CH | Switzerland | Diseases of the respiratory system (J00-J99) | normalized | 1.036089 | 0.920944 | 1.042967 | 1.036311 | 1.022112 | 0.841739 | 0.808238 | 1.010353 | 0.000000 | nan | nan | nan | nan |
notable CODs x Vaccines¶
lets put the CODs on an XY axis with the Vaccine values.
# codxv
temp = codxv[codxv.value < 14].copy()
temp = temp.dropna(subset=['value'])
# display(temp)
codlist = ['Malignant neoplasms (C00-C97)','Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)','Diseases of the respiratory system (J00-J99)']
for c in codlist:
# kde
g = sns.jointplot(
data=temp[temp['filter_x'] == c],
x="value",
y=2022,
kind="kde",
fill=True,
cmap=heatmapCM
)
# Add scatter points on top
g.ax_joint.scatter(
temp[temp['filter_x'] == c]["value"],
temp[temp['filter_x'] == c][2022],
color="black",
alpha=0.7,
s=7
)
min_c = c
if len(min_c) >= 50:
min_c = min_c[0:25] + '...' + min_c[-10::]
g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))",min_c)
g.figure.suptitle(f"Kde Plot : {min_c} x Vaccines", fontsize=14)
# Add a title
# plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)
# Add vertical and horizontal lines
plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1) # Vertical line at x=20
plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1) # Horizontal line at y=3
# display(vdravg)
plt.show()
Post Axis Notes:¶
- Compare the Charts...
- Kde Plot : Death x Vaccines
- Y: Average Normalized Deaths between 2022-2024
- X: Vaccines (ratio (total_dose/pop.))
- Kde Plot : Malignant neoplasms (C00-C97) x Vaccines
- Y: Normalized Malignant neoplasms (aka Cancer) Deaths (in 2022)
- X: Vaccines (ratio (total_dose/pop.))
- Kde Plot : Death x Vaccines
here is the Scatter Chart of Malignant neoplasms (C00-C97) x Vaccine¶
compare it with
title = 'Scatter Chart - Malignant neoplasms (C00-C97) x Vaccine'
display(MD(f'### {title}'))
temp = codxv[codxv.filter_x == 'Malignant neoplasms (C00-C97)']
fig = px.scatter(
temp,
x="value",
y=2022,
color="name",
hover_name="name",
title=title,
labels={
"value": "Vaccines (ratio total_dose/pop.)",
"Malignant neoplasms (C00-C97) 2022": 2022
},
height=750
)
fig.update_layout(template="plotly_dark")
fig.add_shape(
type="line",
x0=0, x1=dxv.value.max(),
y0=1, y1=1, # Start and end y-coordinates
line=dict(color="Red", width=2, dash="dash"), # Line style
xref="x", yref="y" # Reference axes
)
fig.show()
Scatter Chart - Malignant neoplasms (C00-C97) x Vaccine¶
misc_and_style¶
Excess Mortality And Vaccines In Europe (v2) #Table of Contents #Prerequisites ##Scientific Method ##Logical Fallacies ###About Appeal to Authority ####And More ... ###About Post Hoc Ergo Propter Hoc (Correlation vs Causation) #Observation ##News Articles & Headlines ###Ad Hominem Attacks ###Appeals to Authority ###Appeal to Emotions ##Denial of Aid #Hypothesis #below we will be #Setup #Helping Functions #Import and Clean Data ##death data ###Getting the Data ###variables ##cause of death ###Getting the Data ###variables ##vaccine data ###Getting the Data ###variables #combine all data ##variables #Lets Visualize the Data ##Line Charts - normalized deaths ##HeatMaps of deaths by AgeGroups ###Post HeatMaps Notes ##Spearman and Kendall ##Spearman and Kendall ... continued. ##Other death x vaccine charts ##lets look at Deaths for each year 2015-2024 separately ##notes: ##Bad-Batch Theory ###There seems to be to be an unusual grouping/cluster ###Bad-Batch Theory (by Vibeke Manniche) ###my 2 cents #Lets look at some of the Causes of Death ##Hypothesizing Adverse Reactions from an mRNA Vaccine ###Spearman and Kendall ... (again) ###Note: #Lets see the Spearmans Rank over time ##Note: #lets see these overtime #notable CODs x Vaccines ##Post Axis Notes: ##here is the Scatter Chart of Malignant neoplasms (C00-C97) x Vaccine misc_and_style sidelinks { sidelinks a { sidelinks a:hover {